Dsp Lab Manual

DIGITAL SIGNAL PROCESSING LAB MANUAL

ECE: V SEMESTER

LIST OF EXPERIMENTS

MATLAB :-

INTRODUCTION TO MATLAB. GENERATION OF SIGNALS. LINEAR AND CIRCULAR CONVOLUTION OF TWO SEQUENCES. SAMPLING AND EFFECT OF ALIASING. DESIGN OF FIR FILTERS. CALCULATION OF FFT OF A SIGNAL. DESIGN OF IIR FILTERS.

PROCESSOR:-

STUDY OF TMS320C50 PROCESSOR. 16 BIT ARITHMETIC OPERATION USING TMS320C50. STUDY OF VARIOUS ADDRESSING MODES OF DSP USING SIMPLE

PROGRAMMING EXAMPLES. SAMPLING OF INPUT SIGNAL AND DISPLAY. IMPLEMENTATION OF FIR FILTER. CALCULATION OF FFT.

1. STUDY OF MATLAB

AIM:

To study the MATLAB and its operations.

APPARATUS REQUIRED:

PC with software MATLAB

THEORY:

MATLAB is a high performance language for technical computing. It integrates computing, visualization and programming in an easy way to be used by the environment where problems and solutions are expressed in familiar mathematical notation.

Typical uses include

1. Math and Computation

2. Algorithm development

3. Data acquisition

4. Modeling, simulation and proto-typing

5. Scientific and engineering graphics

6. Application development including graphical user interface building

7. Data analysis, exploration, visualization.

MATLAB SYSTEM:

The MATLAB consists of three main parts

1. Desktop and Development Environment

This is the set of tools and facilities that help you use MATLAB function and files. Many of these tools are graphical user interfaces. It includes the MATLAB desktop and command window, command history and editor and debugger, a code analyzer and other reports, and browser for viewing help, the workspace, files and the search path.

2. The MATLAB Mathematical Function Library

This is a vast collection of computational algorithm ranging from elementary function like sum, sine, cosine and complex arithmetic to more sophisticated functions like matrix inverse, matrix Eigen values, Bessel functions and fast Fourier transform.

3. The MATLAB Language

This is a high level matrix/array language with control flow statements, functions, data structures, input and output and object oriented programming features. It allows both programming in the small to rapidly create quick and dirty throw –away program and programming in the large to create large and complex application programmer.

4. Graphics

MATLB has extensive facilities for displaying vectors and matrices as graphs as well as annotating and printing these graphs. It includes high-level functions for two and three dimensional data visualization, image processing animation and presentation graphics.

5. MATLAB External Interfaces

This is a library that allows you to write a C program and function FORTRAN programmer that interacts with MATLAB. It includes facilities for calling routines from MATLAB (dynamic range), calling MATLAB as a computational engine, and for reading and writing MATLAB files.

EXAMPLES:

1. Mathematics-Mathematical operation functions and data analysis.

2. Programming-Programming features and techniques.

RESULT:

Thus the functions and operations of MATLAB was studied.

2.GENERATION OF SIGNALS

AIM:

To draw the waveform for unit step, unit ramp, unit impulse, exponential, sine and cosine wave using MATLAB program.

APPARATUS REQUIRED:

PC with MATLAB

ALGORITHM:

STEP1: Clear output screen by using clc command in editor window.

STEP2: Divide output screen into number of portions by using sub-plot command.

STEP3: Then the function for unit step, ramp, impulse and exponential, sine and cosine waves are

declared.

STEP4: Give the title for each output.

STEP5: End the program.

PROCEDURE:

1. Click MATLAB icon and open a new file.

2. Type the program and save the project.

3. Run the program by clicking on debug.

4. Note the output and plot the graph for the output.

PROGRAM:

clc;

clear all;

figure(1);

subplot(3,2,1);

a=5;

t=-a ; a;

i =[zeros(1,a),1,ones(1,a)];

stem (t, i);

x label (‘time’);

y label (‘amplitude’);

title (‘unit step’);

subplot (3,2,2);

figure (1);

a=5;

t=-a;a;

x=t.i;

stem(t,x);



title (‘unit ramp’);

subplot (3,2,3);

figure (1);

a=5;

t=-a:a;

j=[zeros(1,a),1,zeros(1,a)];

stem (t,j);



title (‘unit impulse’);

subplot (3,2,4);

figure (1);

a=5;

t=-a:a;

x=exp(t);

stem (t,x);



title (‘unit exponential’);

subplot (3,2,5);

t=0:.01:2;

x=sin(2*pi*t);

plot (t,x);

line ([0,2],[0,0]);



title (‘sine wave’);

subplot (3,2,6);

t=0:.01:2;

x=cos(2*pi*t);

plot(t,x);

line([0,2],[0,0]);



title(‘cosine wave’);

RESULT:

Thus the waveform for unit step, unit ramp, unit impulse, unit exponential, and sine and cosine wave obtained using MATLAB program.

3. LINEAR AND CIRCULAR CONVOLUTION OF TWO SEQUENCES

AIM:

To perform linear and Circular convolution.

APPARATUS REQUIRED:

PC with MATLAB

ALGORITHM:

STEP1: Get the input sequence.

STEP2: Plot them using subplots in figure window.

STEP3: Calculate output using convolution command.

STEP4: Plot sequences using subplot command.

STEP5: Stop the program.

THEORY:

LINEAR CONVOLUTION:

Any linear system is implemented by means of linear convolution defined as,

Y (n) =∑x (k) h (n-k)

If the two inputs are each of length n1 and n2 the output is of length n1+n2-1.the linear convolution between the two inputs is equivalent to flipping one input and running it through the other like a train.

CIRCULAR CONVOLUTION:

We know that linear convolution of two finite length data sequence is done by assuming that the data is zero outside the length in which it is defined[recall that linear convolution runs from –α to α].this need not be the only valid assumption. For e.g. one can assume that the data is periodic outside the sequence length in which it is defined. This is consistent with the definition of DFT.DFT is equivalent to discrete time Fourier series and that all Fourier series assume periodicity. So this definition of convolution would be more practical use since it implies with DFT definition. Therefore circular

Convolution is defined

n-1

X (n) +y (n) =∑ x (m) y [(n-m) mod n)]

m=0

PROCEDURE:

1. Click on MATLAB icon and open new file.

2. Type program and save it.

3. Run program by clicking on debug.

4. Note output and plots the graph.

PROGRAM:

%linear convolution

clear all;

clc;

close all;

a=input(‘enter the value of a’);

b= input(‘enter the value of b’);

subplot (3,1,1);

stem (a);

subplot (3,1,2);

stem(b);

c=conv(a,b);

subplot (3,1,3);

stem (c);

%circular convolution

clear all;

clc;

close all;

x= input(‘enter the value of x’);

y= input(‘enter the value of y’);

a=length(x);

b=length(y);

n=a+b/2

%zero padding

if a~=b

if a<b

n=b;

x=[x,zeros(1,b-1)];

else

n=a;

y=[y,zeros(1,a-1)];

end

else

n=a;

end

%circular convolution

for i=1:n

z(i)=0;

for j=1:n

k=mod(i-j(n))+1;

z(i)=z(i)+[x(j)*y(k)];

end

end

subplot (3,1,1);

stem (x);

x label (‘n’);

y label (‘x(n)’);

title (‘sequence 1’);

disp(‘sequence 1 :’);x

subplot (3,1,2);

stem (y);

x label (‘n’);

ylabel (‘y(n)’);

title(‘sequence 2’);

disp(‘sequence 2 :’);y

subplot(3,1,3);

stem (z);

x label (‘n’);

y label (‘z(n)’);

title (‘output’);

disp(‘output :’);z

g.text(‘circular convolution’);

RESULT:

Thus program to perform linear and circular convolution in MATLAB has been verified and processed.

4.SAMPLING AND EFFECT OF ALIASING

AIM:

The sample sine wave and plot it on the screen and study the effect of aliasing using MATLAB.

APPARATUS REQUIRED:

PC with MATLAB.

ALGORITHM:

STEP1: Start the program.

STEP2: Design the value of the signal frequency of sine wave and the sampling frequency.

STEP3: Define the number of samples required and the time of simulation.

STEP4: Calculate the values of sine wave at each time using the appropriate formula.

STEP5: Plot the graph using the stem command.

STEP6: Increase the frequency of sine wave when fo > fs/2 so that aliasing takes place.


THEORY:

According to sampling theorem a band limited signal with highest frequency component of Wm must be sampled (i.e.) Ws=2Wm is not satisfied, and then the signal will be aliased to another signal of linear frequency this is known as aliasing.

PROCEDURE:

1. Click on MATLAB icon and open a new file.

2. Type the program & save the project.



PROGRAM:

%SAMPLING AND EFFECT OF ALIASING

clc;

clear all;

close all;

disp(‘aliasing’);

fc1=50;

fc2=10;

t=0:(1/10)e2:0.1;

x1=sin(2*pi*fc1*t);

x2= sin(2*pi*fc2*t);

subplot (2,3,1);

plot(x1);

line ([0,200],[0,0]);



title (‘sinusoidal signal f=50 hz’);

subplot (2,3,4);

plot (x2);

line([0,100],[0,0]);



title (‘sinusoidal signal f=50 hz’);

fs=40;

n=0:1:15;

x3=sin(2*pi*fc1*n/fs);

x4=sin(2*pi*fc2*n/fs);

subplot (2,3,2);

stem(x3);

line ([0,15],[0,0]);



title (‘sampled signal (alias fs=40hz)’);

subplot (2,3,5);

stem (x4);

line ([0,15],[0,0]);



title(‘sampled signal fs=40 hz’);

fs1=150

n=0:1:10

x5=sin (2*pi*fc1*n/fs1);

x6=sin(2*pi*fc2*n/fs1);

subplot (2,3,3);

stem (x5);

line([0,10],[0,0]);



title(‘sampled signal fs1=150hz’);

subplot(2,3,6);

stem (x6);

line ([0,10],[0,0]);



title(‘sampled signal fs1=150hz’);

g text(‘sampling and aliasing’);

RESULT :

Thus a sine wave is sampled and the effects of aliasing are studied.

5. DESIGN OF FIR FILTERS

AIM:

To write a MATLAB program for FIR filter using different window techniques for the given order and sampling frequency.

APPARATUS REQUIRED:

PC with MATLAB

ALGORITHM:

STEP1: Clear the screen.

STEP2: Sampling frequency & cut off frequency are specified.

STEP3: FIR filter coefficient is determined by using FIR 1 command.

STEP4: Input to the FIR filter is given as a sequence square wave & the output is computed using filter

command.

STEP5: Frequency response of the filter with various windows functions calculated using frequency.

STEP6: From the frequency response various functions are studied.

THEORY:

FIR is the non-recursive filter that has the impulse response for finite number of n, n1 ≤ n≤ n2 with n1 and n2 being finite. This filter is also known as convolution filter b0 and is all non-zero system.

In general, the unit sample response is of finite duration and must be truncated at some point to yield FIR filter of finite length. This method is called as WINDOWING. Thus multiplying a window function in time domain results in the convolution of Fourier transform of window with that of unit sample response, which will be a sine function .In most of times, two criteria that has satisfied by the window functions are-

(i) The width of the main lobe should be small for the ripples to be small.(ii) The height of the side lobe should be small for the ripples to be small.

Thus we will study various windows based on the above criteria.

PROCEDURE:


2. Type the program & save the project.



PROGRAM:

% KAISER WINDOW

clc;

clear all;

close all;

n=input (‘enter the order of the filter’); %order of the filter

wc=input (‘enter cut off frequency’); % cut off frequency

beta=input(‘enter the beta value’); % kaiser window parameter

w=kaiser(n+1,beta); %fir filter using Kaiser window fn

b=fir1(n,wc,w); % fir filter coefficient

[h,omega]=freqz(b,1,256); %frequency response

m=20*log(abs(h)); %magnitude response

ph=angle(h*(180/pi)); %phase response

subplot (2,2,1);

plot (b);

title (‘filter response’);

subplot (2,2,2);

plot (omega/pi,h);

title (‘frequency response’);

subplot (2,2,3);

plot(m);

title(‘magnitude response’);

subplot(2,2,4);

plot(ph);

title(‘phase response’);

%RECTANGULAR WINDOW

clc;

clear all;

close all;

n=input(‘enter the value’)

wc=input(‘enter the cut-off frequency’)

w=boxcar(n+1);

b=fir1(n,wc,w);

[h,omega]=freqz(b,1,256);

m=20*log(abs(h));

ph=angle(h*180/pi));

subplot(2,2,1);

plot(b);

title(‘filter response’);

subplot(2,2,2);

plot(omega/pi,h);

title(‘frequency response’);

subplot(2,2,3);

plot(m);


subplot(2,2,4);

plot(ph);


%BARLETT WINDOW

clc;

clear all;

close all;

n=input(‘enter the value’);

wc=input(‘enter cut off frequency’);

w=barlett(n+1);

b=fir1(n,wc,w);


m=20*log(abs(h));

ph=angle(h*(180/pi));

subplot(2,2,1);

plot(b);

title(‘h(n)’);

subplot(2,2,2);

plot(omega/pi,h);


subplot(2,2,3);

plot(m);


subplot(2,2,4);

plot(ph);


%BLACKMAN WINDOW

clc;

clear all;

close all;

n=input(‘enter the value’);

wc=input(‘enter the cut off frequency’);

w=blackman(n+1);

b=fir1(n,wc,w);


m=20*log(abs(n));

ph=angle(h*(180/pi));

subplot(2,2,1);

plot(b);

title(‘h(n)’);

subplot(2,2,2);

plot(omega/pi,h);


subplot(2,2,3);

plot(m);


subplot(2,2,4);

plot(ph);


RESULT:

Thus the FIR filter was designed using window technique using MATLAB program for the given order and cut –off frequency.

6. CALCULATION OF FFT OF A SIGNAL

AIM:

To perform fast Fourier transforms using DFT using MATLAB program.

APPARATUS REQUIRED:

PC with MATLAB

ALGORITHM:

STEP1: Get the input sequence.

STEP2: Plot them using subplots in the figure window.

STEP3: Find the result using FFT commands.

STEP4: Plot the sequences using subplot command.

STEP5: Stop the program.

THEORY:

The FFT is the faster version of the discrete Fourier transform (FFT).The FFT utilizes same algorithm to do the same thing as DFT, but in much less time. The DFT is extremely imp in the area of frequency analytic because it takes a discrete signal in time domain and transform that signal into it discrete frequency domain representation. Without the discrete time, to discrete time freq transform, we could not be able to compute FFT in DFT based system. It is the speed and discrete nature of the FFT that allows us to analyze the signal spectrum with MATLAB.

PROCEDURE:

1. Click on MATLAB icon and open a new file

2. Type the program and save the project

3. Run the program by clicking debug.

4. Note the output and plot the graph.

PROGRAM:

%N POINT FFT USING DFT:

clc;

clear all;

close all;

x=input(‘enter the sequence’);

N=length(x);

z= fft(x,n);

display(‘input’);x

display(‘output’);z

n=1:N;

subplot(3,2,1);

stem(n,x);

xlabel(‘sequence number’);

ylabel(‘amplitude’);

title(‘input sequence’);

subplot(3,2,2);

stem(n,abs(z));



title(‘output amplitude’);

subplot(3,2,3);

stem(b,real(z));


ylabel(‘real’);

title(‘output real part’);

subplot(3,2,4);

stem(n,imag(z));


ylabel(‘imaginary’);

title(‘output imaginary part’);

subplot(3,2,5);

stem(n,(z));


ylabel(‘phase’);

title(‘output phase’);

%N-POINT INVERSE FFT USING IDFT:

clc;

clear all;

close all;

x=input(‘enter the number’);

N=length(x);

z= ifft(x,n);

display(‘input’);x

display(‘output’);z

n=1:N;

subplot(2,2,1);

stem(n,x);



title(‘input sequence’);

subplot(2,2,2);

stem(n,z);



title(‘output sequence’);

subplot(2,2,3);

stem(n,floor(real(acos(z))));


ylabel(‘amp’);

title(‘output sequence’);

gtext(‘mnmjec’);

RESULT:

Thus the program to perform linear combination and circular convolution using FFT has been processed and verified successfully.

7. DESIGN OF IIR FILTERS

AIM:

To write MATLAB program for IIR filter for the given order and cut off frequency and stop band frequency.

APPARATUS REQUIRED:

PC with MATLAB

ALGORITHM:

STEP1: Get the required filter parameters.

STEP2: Calculate filter co-efficient.

STEP3: Butterworth filter can be designed using butter command

[b a]=butter (n, w0), n=order of filter and w0=normalized cut off frequency.

STEP4: Chebyshev filter can be designed using cheby1 and cheby2 command.

[b a]=cheby1(n,r,w0)

n=order of filter, r=pass band ripple and w0=normalized cut off frequency.

STEP5: plot the frequency, magnitude and phase response.


THEORY:

BUTTERWORTH FILTER:

Butterworth filter approximation concentrates on w=0 (i.e.) maximally flat response at origin.

Hn(w)=wn

|π(jw)|2 =1/(1+c2w2n)

As the order of filter is increased the sharpness of cut off increase. The attenuation in pass band is xp(w) =10log (1+c2).butter worth filter gives a fairly linear response. As order of chebyshev response becomes higher, phase response become non-linear.

CHEBYSHEV FILTER:

Concentrates over range 0<w<1 rather than w=0.the same characteristics of Butterworth filter can be achieved using a filter of lesser order. It has ripples in pass band and stop band.

Tn (w) =cos (n cos-1w), |w|<1 =cosh (ncosh-1w), |w|≥1

Tn+1(w)=2w Tn(w)-Tn-1(w)

PROCEDURE:


2. Type the program and save the project.



PROGRAM:

%CHEBYSHEV 1 FILTER

clc;

close all;

clear all;

n=input(‘enter the order value’); %order of the filter

wc=input(‘enter the cut off frequency’); %cut off frequency

r=input(‘enter stop band ripple’); %stop band frequency

[b, a]=cheby1(n,r,wc,’s’); %chebyshev function

[h,omega]=freqz(b,a,256); %frequency response

m=20&log(abs(h)); %magnitude response

subplot(2,2,2);

plot(m);grid


subplot(2,2,1);

plot(omega/pi,h);grid



subplot(2,2,3);

plot(ph);grid


gtext(‘chebychev1=IIR filter’);

%CHEBYSHEV 2 FILTER

clc;

close all;

n=input(‘enter the order value’); %order of the filter


r=input(‘enter passband ripple’); %pass band frequency

[b, a]=cheby2(n,r,wc,’s’); %chebyshev function

[h,omega]=freqz(b,a,256); %frequency response

m=20*log(abs(h)); %magnitude response


subplot(2,2,1); %plot response

plot(omega/pi,h);grid


subplot(2,2,2);

plot(m);grid


subplot(2,2,3);

plot(ph);grid


%BUTTERWORTH FILTER

clc;

close all;

clear all; %clear the screen

n=input(‘enter the value’); %order of the filter


[b,a]=butter(n,wc); %butterworth function

[h.omega]=freqz(b,a,256); %frequency response

m=20*log((abs(h)); %magnitude response

ph=angle(h*(180/pi)) %phase response

subplot(2,2,1);

plot(omega/pi,h);grid %plot the response


subplot(2,2,2);

plot(m);grid


subplot(2,2,3);

plot(ph);grid


gtext(‘BUTTERWORTH IIR FILTER’);

RESULT:

Thus the programs for IIR filter are designed successfully using MATLAB programs for the given order, cut off frequency and stop band frequency.

8.STUDY OF TMS320C50 PROCESSOR

ARCHITECTURE:

In a strict Harvard architecture, program and data memory are in two separate spaces, permitting a full overlap of instruction fetch end execution. The TMS320 family’s modification of the Harvard architecture allows transfer between program and data spaces, thereby increasing the flexibility of the device. The modification permits coefficients stored in program memory to be read into data RAM, eliminating the need for a separate coefficient ROM. It also makes available immediate instructions and subroutines based on computed values.

32- BIT ACCUMULATOR:

The TMS320C50 contains a 32 bit ALU and accumulator for support of double- precision, two’s complements arithmetic. The ALU is a general purpose arithmetic unit that operates on 16 bit words taken from the data RAM or derived from immediate instructions. The accumulator stores the output from the ALU and is often an input to the ALU. Its word length is 32- bit. The accumulator is divided into a high order word (bits 31 through 16) and a low order word (bits 15 through 0). Instructions are provided for storing and loading the high and low order accumulator words to memory.

16x16 BIT PARALLEL MULTIPLIER:

The multiplier performs a 16x16 bit two’s complement multiplication with a 32-bit result in a single instruction cycle .The multiplier consists of three units: theT-Register, P-Register, and multiplier array. Multiplier values either come from the data memory or are derived immediately from the MPY (multiply immediate) instruction word. The fast on-chip multiplier allows the device to perform fundamental operation such as convolution, correlation and filtering. Two multiply /accumulator instructions in the instruction set fully utilize the computational bandwidth of the multiplier, allowing both operands to be processed simultaneously.

SHIFTERS:

A 16-bit scaling shifter is available at the accumulator input. This shifter produces a left shift of 0 to 16 bits on the input data to accumulator .in addition one shifter at the output of P-register, can shift the product by 1or 4-bits left or 6-bits right, before transferring the product to accumulator.

DATA AND PROGRAM MEMORY:

Since the TMS320C50 use Harvard architecture, data and program memory reside in two separate spacers. Additionally TMS320C50 has one more memory space called I/O memory space. The total memory capacity of TMS320C50 is 64KW each of Program, Data and I/O memory. The 64KW of data memory is divided into 512 pages with each page containing 128 words. Only one page can be active at a time .TMS320C50 has 1056 words of dual access on chip data memory is divided as three blocks B0, B1,& B2 of which B0 can be configured as program or data RAM.

INTERRUPTS AND SUBROUNTINE:

The TMS320C50 has three external makeable user interrupts available for external devices that interrupt the processor .the TMS320C50 contains an eight-level hardware stack for saving the contents of the program counter during interrupts and subroutine calls.

SERIAL PORT:

A full-duplex on-chip serial port provides direct communication with serial devices such as codec’s, serial A/D converters and other serial systems. The interface signals are compatible with codec’s and many others serial devices with a minimum of external hardware.

INPUT AND OUTPUT:

The 16-bit parallel data bus can be utilized to perform I/O functions in two cycles. The I/O ports are addressed by four LSBs on the address lines, allowing 16 input and 16 output ports. In addition, a polling input for bit test and jump operation (BIO) and three interrupt pins (INT0-INT2) have been incorporated for multitasking.

EXAMPLES OF ARITHMETIC OPERATIONS USING TMS320C50

PROGRAM:

ADDITION:

.MMREGS

.TEXT

START: LDP #1004

LACC #1111H

ADD #2222H

SACL 0H

HLT: B HLT

SUBTRACTION:

.MMREGS

.TEXT

START: LDP #100H

LACC #2222H

SUB #1111H

SACL 0H

HLT: B HLT

MULTIPLICATION:

.MMREGS

.TEXT

START: LDP #100H

LACC #037AH,0

SACL 0000,0

LACC # 012EH,0

SACL 0001,0

LT 0000

MPY 0001

PAC

SACL 0002,0

SACH 0003,0

HLT: B HLT

RESULT:

Thus the architecture of TMS320C50 was studied with the simple programming examples

9. STUDY OF VARIOUS ADDRESSING MODES OF DSP USING SIMPLE PROGRAMMING EXAMPLES

AIM:

To study the various addressing modes of DSP processor using simple programming examples.

APPARATUS REQUIRED:

1. TMS320C5X

2. PC

3. RS232 CABLE

TYPES OF ADDRESSING MODES:

1. Direct addressing

2. Memory map register addressing

3. Indirect addressing

4. Immediate addressing

5. Dedicated register addressing

6. Circular addressing

PROCEDURE:

1. Click on C50 debugger icon and start a new project.

2. Type the program and save it as an assembly file.

3. Program is executed by giving G0C000 in communication window.

4. The output is obtained.

5. Values are obtained by giving various input values.

PROGRAM:

IMMEDIATE ADDRESSING MODE:

.MMREGS

ENTRY

.LACC #1000H

.LACC #1111H,3

LAR AR0,#1000H

LAR AR1,#1100H

ADD #00FFH

ADD #0011H,2

SPLK #10H,0

MPY #0010H

SUB #0022H

SUB #0011H,3

.END

DIRECT ADDRESSING MODE:

.MMREGS

.ENTRY

LDP #20H

LACC 10H

LACC 5H,2

LDP #22H

LAR AR0,15H

SACL 15H

SACL 20H,3

SAMM AR7

LDP # 12H

ADD 25H

ADD 7H,2

SUB 10H

SUB 12H,2

SPLK #10H,0

MPY 15H

.END

INDIRECT ADDRESSING MODE:

.MMREGS

.PS0A00H

.ENTRY

LAR AR0,#1000H

LACC*

LACC*,4,AR1

LAR AR1,#1010H

SACL*,

SACL*+,2,AR0

LACC*-,2,AR1

LACC*0+

LACC*BR0+

ADD*+,0,AR0

SUB*-,2

SPLK #10H,0

MPY*

.END

CIRCULAR ADDRESSING MODE:

.MMREGS

.PS0A00H

.ENTRY

SPLK #1000H,0

SPLK #1003H,1

SPLK #08H,2

LACC*+

LACC*+

LACC*+

LACC*+

.END

RESULT:

Thus the various addressing modes of DSP processor using simple programming examples are studied and verified.

10. SAMPLING OF INPUT SIGNAL AND DISPLAY

AIM:

To write a program to sample and display an analog signal using TMS320C50 KIT.

APPARATUS REQUIRED:

1. TMS320C5X

2. PC

3. RS232 CABLE

PROCEDURE:



3. The input signal is given to the DSP kit.


5. The sampled output can be viewed in CRO.

PROGRAM:

DATA .SET 2H

DELAY .SET3H

.MMREGS

.TEXT

START: LDP#100H

LAR AR0,#9000H

LAR AR1,#719H

REP: IN 0,06

RPT #OFH

NOP

IN 0,04

SPLK #00FFH,DELAY

RPT DELAY

NOP

LACC 0

AND #0FFFH

MAR*,AR0

SACL*+,0,AR1

BANZ REP,*-

CONT1: LAR AR0,#9000H

LAR AR1,#719H

LOOP:MAR*,AR0

RPT #1FFH

NOP

OUT*+,04,AR1

BANZ LOOP,*-

B CONT1

RESULT:

Thus the input signal was sampled and displayed.

11.IMPLEMENTATION OF FIR FILTER

AIM:

To implement a low pass FIR filter using rectangular window using TMS320C50.

APPARATUS REQUIRED:

1. TMS320C5X

2. PC

3. RS232 CABLE

PROCEDURE:




4. The output is obtained


PROGRAM:

.MMREGS

.TEXT

B START

CTABLE:

.WORD 0FFF4H

.WORD 0FFE3H

.WORD 0FFCFH

. WORD 0FEBBH

. WORD 0FFA7H

. WORD 0FF97H

.WORD 0FF8EH

. WORD 0FF8FH

. WORD 0FF9BH

. WORD 0FFB7H

. WORD 0FFE3H

. WORD 022H

. WORD 075H

. WORD 0DCH

. WORD 0157H

. WORD 01E5H

. WORD 0283H

. WORD 032FH

. WORD 03E5H

. WORD 04A0H

. WORD 055CH

. WORD 0614H

. WORD 06C2H

.WORD 0761H

. WORD 076BH

.WORD 085BH

.WORD 085BH

. WORD 07EBH

. WORD 07E1H

. WORD 06C2H

. WORD 0614H

. WORD 055CH

. WORD 04A0H

. WORD 03E5H

. WORD 032FH

. WORD 0283H

. WORD 01E5H

. WORD 0157H

. WORD 0DCH

. WORD 075H

. WORD 022H

. WORD 0FFE3H

. WORD 0FFB7H

. WORD 0FF9BH

. WORD 0FF8FH

. WORD 0FF8EH

. WORD 0FF97H

. WORD 0FFA7H

. WORD 0FFBBH

. WORD 0FFCFH

. WORD 0FFE3H

. WORD 0FFF4H

START: MAR*,AR0

LAR AR0,#0200H

RPT #33H

BLKP CTABLE,*+

SETC CNF

ISR: LDP #0AH

LACC #0

SACL 0

OUT 0,05

IN 0,06H

LAR AR7,#0

MAR*,AR7

BACK:

BANZ BACK,*-

IN 0,4

NOP

NOP

NOP

NOP

MAR*,AR1

LAR AR1,#0300H

LACC0

AND #0FFFH

SUB #800H

SACL*

LAR AR1,#333H

MPY #0

ZAC

RPT #33H

MACD 0FF00H,*-

APAC

LAR AR1,#0300H

SACH*

LACC*

ADD #800H

SFR

SACL*

OUT*,4

LACC #0FFH

SACL 0

OUT0,05

NOP

B ISR

HLT: B HLT

RESULT :

Thus the FIR filter was designed using TMS320C50 DSP Kit.

12. CALCULATION OF FFT

AIM:

To perform DIT fast Fourier transform for 4 point sequence using TMS320C50 processor.

APPARATUS REQUIRED:

1. TMS320C50

2. PC

3. RS232 CABLE

ALGORITHM:

STEP1: processor initialization.

STEP2: Get the 4 input samples from data memory.

STEP3: Apply bit reversal to these samples.

STEP4: multiply the bit –reversed samples with twiddle factors.

STEP5: Store the real part of output and imaginary part of output.

STEP6: End of program.

PROCEDURE:




4. The output is obtained and verified.

5. Build the program and load it into the processor.

6. The inputs can be given by SD command in communication window.


PROGRAM:

IN.SET 8010H

BITREV .SET 8020H

REA L.SET 8040H

IMG: SET 8050H

.MMREGS

.TEXT

START: LDP # 100H

LAR AR1 ,#IN

LAR AR2,#BITREV

SPLR #2H,05H

LMMR INDX,#8005H

MAR*,AR2

RPT #3H

BLDD #IN,*BR0+

LAR AR2 ,#BITREV

LAR AR3 ,#8030H

LAR AR0 ,#1H

FFT1: MAR*,AR2

LACC*+

SACB

LT*+

MPY #1H

APAC

MAR*,AR3

SACL*+

LACB

SPAC

SACL*+,AR0

BANZ FFT1,*-

LAR AR3,#8030H

LAR AR4,#REAL

LAR AR5,#IMG

MAR*,AR3

LACC*

SACB

ADRK#2H

LT*-

MPY #1H

APAC

MAR*,AR4

SACL*

ADRK #2H

LACC #0H

MAR*,AR5

SACL*

ADRK #2H

LACB

SPAC

MAR*,AR4

SACL*-

LACC #0H

MAR*,AR5

SACL*-,AR3

LACL*,AR4

SACL*,AR3

ADRK #2H

LT*

MPY #0FFFFH

MAR*,AR5

SPL8,AR3

LT*

MPY #1H

MAR*,AR5

ADRK #2H

SPL*

HLT: B HLT

INPUTS: OUTPUTS:

(BIT REVERSED OUTPUT)

8010 :0000 8020 :0000

8010 :0000 8020 :0002

8010 :0000 8020 :0001

8010 :0000 8020 :0003

(REAL PART) (IMAGINARY PART)

8040 :0006 8050 :0000

8041 :FFFE 8051 :0002

8042 :FFFE 8052 :0000

8043 :FFFE 8053 :FFFE

RESULT:

Thus the FFT of the inputs were performed successfully using DSP processor.

Dsp Lab Manual

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