SR

NO

EXPERIMENT NAME:

PAGE NO.

DATE

SIGNATURE

1 Signal Generation. 1

2 Signal Operation

6

3 Convolution and Correlation 14

3 Discrete Fourier Transform 23

5 Application of DSP 27

6 Study of Filter Design and analysis tools.

30

7 Design filter with given specifications 41

8 CC Studio and TI6x architecture 47

9 Higher level language program in CCS. 49

10 Different programming techniques in CCS.

52

11 DSK 6713 KIT

56

LAB 1

Signal Generation

AIM: Generate analog and digital signals.

RESPECTIVE MATLAB FUNCTIONS:- Graphics Function Linespace(0,2*pi,100) %create a linearly space 100 elements between 0 to 2*pi fplot %plot a function of a single variable hist %makes histograms bar %crate a bar graph pie %create a pi chart polar %plot curves in polar co-ordinates semilogx %make semi log plot with log scale on x axis semilogy %make semi log plot with log scale on y axis loglog %create plot with log scale on the both axis stem %plot a stem(discrete) graph area plot %plot the graph plot with style opti color style %select the color line style marker style subplot (m,n,p) %breaks the Figure window into an m-by-n matrix of small axes, selects the p-th axes for the

current plot xlabel %label on the x-axis ylabel %label on the y-axis title %title of the graph text %write notes at a specified location legend %produce a boxed legend on a plot hold on %hold the graph on figure window hold off axis %set the limits of the axis equal %sets equal scale on the both axis square %sets the default rectangular frame to a square normal %reset the axis to default values Functions for signal generations: Sin, cos, zeros, stem, ones, square, exp

Matlab codes for generating different signals and their figures:

1. Generating a Sine wave.

f=50; t=0:0.0001:0.1; y=sin(2*pi*f*t); plot(t,y)

2. Generating a Cosine wave.

f=50; t=0:0.0001:0.1; y=cos(2*pi*f*t); plot(t,y)

3. Generating a Square wave.

f=50; t=0:0.0001:0.0625; y=square(2*pi*f*t); plot(t,y)

4. Generating a Ramp wave.

t=0:1:10; x=t; plot(t,x)

5. Generating a Impulse wave.

t=-10:1:10; z=[zeros(1,10) 5 zeros(1,10)]; stem(t,z)

6. Generating a Unit Step wave.

t=-10:1:10; z=[zeros(1,10) 1 ones(1,10)]; stem(t,z)

7. Generating a Exponential wave.

n=0:0.1:5; y=exp(n) plot(y) xlabel('Time') ylabel('Amplitude') title('Exponential Wave')

8. Generating a Sinc wave.

t=-4*pi:0.001:4*pi; y=sinc(t) plot(t,y)

CONCLUSION:__________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________ ____________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________ ____________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________

Lab 2

Signal Operation

AIM: Generate analog and digital signals

1) signal shifting clc; clear all; close all; e=input('enter the range for ramp function\n'); z=0:e; x=[z]; n=input('entr shifting index\n'); y=length(x); subplot(1,2,1); stem(x); xlabel('original signal') if n

(2) Left shift:

Output screen:

2) Signal addition

clc; close all; clear all; x=input('enter signal 1 value\n'); y=input('enter signal 2 value\n'); n1=length(x); n2=length(y); subplot(2,2,1); stem(x); xlabel('signal x'); subplot(2,2,2); stem(y); xlabel('signal y'); if n1>n2 y=[y zeros(1,n1-n2)]; else x=[x zeros(1,n2-n1)]; end z=x+y; subplot(2,2,3); stem(z); xlabel('addition of x and y'); for unequal length of signal:

Output screen:

For equal length of signal:

Output screen:

3) Signal Folding

clc; close all; clear all; x=input('Enter siganl samples') p=input('Enter starting point') a=length(x) t=p:1:(p+a-1) subplot(2,1,1) stem(t,x) l=t.*-1; subplot(2,1,2) stem(l,x)

Output screen:

4) Signal Multiplication

clc;

close all;

clear all; x1=input('Enter siganl 1 samples\n'); p=input('Enter starting point signal 1 \n'); n1=p:(p+length(x1)-1); x2=input('Enter siganl 2 samples\n'); q=input('Enter starting point signal 2 \n'); n2=q:(q+length(x2)-1); n=min(min(n1),min(n2)):max(max(n1),max(n2)); y1=zeros(1,length(n));y2=y1;

y1(find((n>=min(n1))& (n=min(n2))&(n

ASSIGNMENTS

1. X(n)=2*delta(n+2)-delta(n-4) clc; close all; clear all; n=-5:5 x1=2*[zeros(1,3) 1 zeros(1,7)] subplot(1,3,1) stem(n,x1) xlabel('time') ylabel('amplitude') x2=(-1)*[zeros(1,9) 1 0] subplot(1,3,2) stem(n,x2) xlabel('time') ylabel('amplitude') x=x1+x2 subplot(1,3,3) stem(n,x) title('final x(n)') Output Screen:

2 .Plot x(n) =n*[u(n)-u(n-10)]+10*exp((-0.03)*(n-10))

clc; clear all; n=0:20; x1=[1 ones(1,20)]; subplot(2,3,1); stem(n,x1); title('Lab2 assgn' ) x2=[zeros(1,10) ones(1,11)]; subplot(2,3,2); stem(n,x2); x3=(exp(-0.3)*(n-10)); subplot(2,3,3); stem(n,x3); y1=n.*(x1-x2); subplot(2,3,4); stem(n,y1); y2=10*x3; subplot(2,3,5); stem(n,y2); y=y1+y2; subplot(2,3,6); stem(n,y);

xlabel('Time'); ylabel('Amplitude'); Output Screen:

(Stepwise output of the function: x(n) =n*[u(n)-u(n-10)]+10*exp((-0.03)*(n-10)) 3. Plot x(n)=cos(0.04*pi*n)+0.2*w(n). clc; clear all; n=0:50; pi=3.14; x1=cos(n*0.04*pi); subplot(3,1,1); stem(n,x1); title('lab2_Assgn2'); x2=rand(1,51); x3=(0.2)*x2; subplot(3,1,2); stem(n,x3); y=x1+x3; subplot(3,1,3); stem(n,y); xlabel('Time'); ylabel('amplitude'); Output Screen:

(Stepwise output of the function: x(n)=cos(0.04*pi*n)+0.2*w(n).))

CONCLUSION:______________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________

LAB-3

Convolution and Correlation

AIM: To perform the operation of convolution. THEORY: Conv- Convolution and polynomial multiplication Syntax= conv(u,v) w= conv(u,v) convolves vectors u and v. Algebraically, convolution is the same as operation as multiplying the polynomials whose coefficients are the elements of u and v. Definition: Let m=length (u) and n=length (v). Then w is the vector of length (m+n-1) whose kth element is the sum over all the values of j which lead to legal subscripts for u(j) and v(k+1-j), specifically j= max(1,k+1-n): min(k,m). When m=n, this gives w(1) = u (1)*v (1) w(2) = u (1)*v (2)+u(2)*v(1) w(3)= u(1)*v(3)+u(2)*v(2)+u(3)*v(1) ... w(n)= u(1)*v(n)+u(2)*v(n-1)+...+u(n)*v(1) ... w(2*n-1)= u(n)*v(n) The convolution theorem says, roughly, the convolving two sequences is the same as multiplying their Fourier transforms. In order to make this precise, it is necessary to pad the two vectors with zeros and ignore round off error. Thus, if X=fft([x,zeros(1,length(y)-1)]) and Y=fft([y,zeros(1,length(y)-1)]) then conv(x,y)=fft(X*Y).

Convolution:

x=[1 2 2 1]; h=[1 0 2]; z=conv(x,h) z = 1 2 4 5 4 2 ---------------------------------------------------------------------------------------------------- x=[1 2 2 1]; h=[1 0 2]; z=conv(h,x) z = 1 2 4 5 4 2 ---------------------------------------------------------------------------------------------------- x=[1 2 2 1]; h=[1 0 2]; z=conv(fliplr(x),h) z = 1 2 4 5 4 2 ---------------------------------------------------------------------------------------------------- x=[1 2 2 1]; h=[1 0 2]; z=conv(fliplr(h),x) z = 2 4 5 4 2 1

Observation: 1) Here from this example we conclude that changing order of functions will not change the convolution of both. x*h=h*x 2)Here x is symmetric so convolution of x(-n) * h(n) = x(n) * h(n) But h is not symmetric. So same is not true for h(-n).it will result in to different answer. Matlab Code:-

Convolution using function x= [1 2 2 3]; y= [2 -1 3]; z=conv (x,y); stem(z); xlabel(Time); ylabel(Amplitude); title(Convolution);

Convolution using function implementation:

u= [1 2 2 3]; v= [1 -3 4]; m=length (u); n=length (v); for k=1:m+n-1; w(k)=0; for j=max(1,k+1-n): min (k,m); w(k)=w(k)+(u(j)*v(k+1-j)); end end stem(w); xlabel (Time);

ylabel (Amplitude); title (Convolution);

OUTPUT:

Correlation x=[1 2 2 1]; h=[1 0 2]; z=xcorr(x,h) z = -0.0000 2.0000 4.0000 5.0000 4.0000 2.0000 1.0000 ---------------------------------------------------------------------------------------------------- x=[1 2 2 1]; h=[1 0 2]; z=xcorr(h,x) z = 1.0000 2.0000 4.0000 5.0000 4.0000 2.0000 -0.0000 ---------------------------------------------------------------------------------------------------- x=[1 2 2 1]; h=[1 0 2]; z=xcorr(x,x) z = 1.0000 4.0000 8.0000 10.0000 8.0000 4.0000 1.0000 ---------------------------------------------------------------------------------------------------- x=[1 2 2 1]; h=[1 0 2]; z=xcorr(h,h) z = 2 0 5 0 x=[1 2 2 1]; h=[1 0 2]; z=xcorr(x,fliplr(h))

z = -0.0000 1.0000 2.0000 4.0000 5.0000 4.0000 2.0000 ---------------------------------------------------------------------------------------------------- x=[1 2 2 1]; h=[1 0 2]; z=xcorr(fliplr(x),h) z = -0.0000 2.0000 4.0000 5.0000 4.0000 2.0000 1.0000 Observation: Here from these examples we conclude that in correlation, 1)z(n) = x(n) ** h(n) => z(-n) = h(n) ** x(n) 2) Auto correlation is having even symmetry. Rxx(n) = Rxx(-n) Correlation using inbuilt function: A=[1 2 3 4]; B=[1 2 3 4]; s2=xcorr(A,B); stem(s2); xlabel('value of k') ylabel('value of s2(k)') title('correlation')

ASSIGNMENTS:

1. Perform correlation operation without using functions. Ans. Correlation is convolution of first and folded second sequence.

u= [1 2 3 4]; z= [1 2 3 4]; a=0:3; [v,b]=sigfold (z,a); %use function to fold signal (like in lab 2) m=length (u); n=length (v); for k:m+n-1; w(k)=0; for j=max (1,k+1-n): min (k,m); w(k)=w(k)+ (u(j)*v(k+1-j)); end end stem(w); xlabel (value of k); ylabel (value of w(k)); title (correlation);

RADAR SYSTEM:

To understand concept of correlation and to understand its use in RADAR application.

RADAR is acronym of Radio Detection And Ranging.As its full name says RADAR is used to detect if any object is there or not in the path of transmission of wave and to know how far it is.

Above task can be done in this way:

1)We transmit radio wave.After some delay time we can receive the reflected wave.

2)We also know the propagation velocity of transmitted wave.

3)So,according to v=d/t, we can calculate the distance of object from transmission place.

CODE:

clc;

clear all;

close all;

D=15; % Delay amount

x=[0 1 2 3 2 1 0]; %Triangle pulse transmitted by radar

n=[-3 -2 -1 0 1 2 3];

% n=[0 1 2 3 4 5 6];

[nd xd]=sigshift(x,n,D); % x(n-D)

figure;

subplot(2,1,1);

stem(n,x);

title('Original Signal');

subplot(2,1,2);

stem(nd,xd);

title('Delayed Signal');

w=rand(1,length(x)); % Random noise w(n)

nw=nd;

figure;

stem(nw,w);

title('Noisy Signal');

% If object is present we receive the signal y(n)

[ny y]=sigadd(xd,nd,w,nw); % Received signa at radar y(n) = x(n-D) + w(n)

figure;

stem(ny,y);

title('Received Noisy Signal');

[nrxy rxy]=corr1(x,n,y,ny); % Cross correlaiton between x(n) and y(n)

figure;

subplot(2,1,1);

stem(nrxy,rxy);

title('Correlation in the Presenance of Object');

% If object is absent we recive only noise signal w(n)

subplot(2,1,2);

[nrxw rxw]=corr1(x,n,w,nw); % Cross correlation between x(n) and w(n)

stem(nrxw,rxw);

title('Correlation in the absenance of Object');

For executing above program function files shown below should be made:

1) function [n,y]=conv1(x1,n1,x2,n2)

nmin=min(n1)+min(n2); % Lowest index of y(n)

nmax=max(n1)+max(n2); % Highest index of y(n)

n=nmin:nmax;

y=conv(x1,x2);

2) function [n,y]= corr1(x1,n1,x2,n2)

[n3,x3]=timereversal(x2,n2); %Folding of x2(n)

[n,y]=conv1(x1,n1,x3,n3); %convolution

3) function [n,y]= sigadd(x1,n1,x2,n2)

m1=min(n1);

m2=min(n2);

st=min(m1,m2);

l1=max(n1);

l2=max(n2);

en=max(l1,l2);

n=st:en;

y1=zeros(1,length(n));y2=y1;

y1(find((n>=m1)&(n=m2)&(n

Graphical Representation of various signals:

Figure 1

Figure 2:

Figure 3:

CONCLUSION:______________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________ ____________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________

Lab-4

Discrete Fourier Transform

AIM: To Perform operation of DFT & IDFT with and without MATLAB inbuilt function. THEORY: The DSP processors are used to perform frequency analysis of signals. To do the frequency analysis we should convert the time domain signal into the frequency domain. For this , the different frequency transformation techniques are used. Using DTFT (discrete Time Fourier transform) we can convert the discrete time domain sequence into frequency Domain. But X(w) is a continuous function of frequency and therefore it is not a convenient Representation of x(n). So, we represent sequence x(n) by samples of its spectrum X(w). Such a frequency domain Representation leads to DFT.(Discrete Fourier transform). N-1 X(K)= x(n) e(-j 2*pi*k*n)/N , K=0,1,2,.... N-1 n=0 N-1 x(n)= 1/N X(K) e(j 2*pi*k*n)/N , n=0,1,2,.... N-1 K=0 DFT computes N equally spaced frequency samples of the DTFT. Both the indices n and K are ranging from 0 to N-1.The integer n is known as time index since it denotes the time instant. The integer K denotes discrete frequency and is called frequency index. RESPECTIVE MATLAB FUNCTIONS: FFT(X) : is the discrete Fourier transform (DFT) of vector X. For matrices, the FFT operation is applied to each column. FFT(X,N): is the N-point FFT, padded with zeros if X has less than N points and truncated if it has more. FFT(X,[ ],DIM) or FFT(X,N,DIM) applies the FFT operation across the dimension DIM. Y = fftn(X) returns the discrete Fourier transform (DFT) of X, computed with a multidimensional fast Fourier transform (FFT) algorithm. The result Y is the same size as X. Y = fftn (X,siz) pads X with zeros, or truncates X, to create a multidimensional array of size siz before performing the transform. The size of the result Y is siz. Y = fftshift(X) rearranges the outputs of fft, fft2, and fftn by moving the zero-frequency component to the center of the array. It is useful for visualizing a Fourier transform with the zero-frequency component in the middle of the spectrum. Y = fftshift(X,dim) applies the fftshift operation along the dimension dim. y = ifft(X) returns the inverse discrete Fourier transform (DFT) of vector X, computed with a fast Fourier transform (FFT) algorithm. If X is a matrix, ifft returns the inverse DFT of each column of the matrix. DFT and IDFT without using MATLAB inbuilt function: %%%%%%%%%%%%%%%%DFT%%%%%%%%%%%%%%%% x=[1 2 3 4]; N=length(x); for k=1:N y(k)=0; for n=1:N y(k)=y(k)+(x(n)*(exp(-i*2*pi*(k-1)*(n-1)/N))); end end disp(y) Answer:: 10.0 -2.0000 + 2.0000i -2.0000 - 0.0000i -2.0000 - 2.0000i %%%%%%%%%%%%%%%%IDFT%%%%%%%%%%%%%%%% x=[10 -2+i*2 -2 -2-i*2]; N=length(x); for n=1:N y(n)=0; for k=1:N y(n)=y(n)+(x(k)*(exp(i*2*pi*(k-1)*(n-1)/N))); end y(n)=y(n)/N; end disp(y) Answer:: 1.0000 2.0000 + 0.0000i 3.0000 - 0.0000i 4.0000 - 0.0000i DFT without using MATLAB inbuilt function: CODE: x=[1,2,3,4] subplot(3,1,1) stem(x) title('x(n)') y=fft(x) subplot(3,1,2) stem(y) title('Fast Fourier Transform of x(n)') subplot(3,1,3)

z=ifft(y) stem(z) title('Inverse Fourier Transform of x(n)') OUTPUT:

DFT without using MATLAB inbuilt function: FUNCTION: function[Xk]=dft(xn,N) n=0:1:N-1 k=0:1:N-1 wN=exp(-j*2*pi/N) nk=n'*k wNnk=wN.^nk Xk=xn*wNnk CODE: xn=[1,2,3,4] N=4 [Xk]=dft(xn,N) a=real(Xk) b=imag(Xk) subplot(2,2,1) stem(a) title('Real Part of fft(x)') subplot(2,2,2) stem(b) title('Imaginary Part of fft(x)') OUTPUT:

IDFT without using MATLAB inbuilt function: Function: function[Xn]=idft(Xk,N) n=0:1:N-1 k=0:1:N-1 wN=exp(j*2*pi/N) nk=n'*k wNnk=wN.^nk Xn=Xk*wNnk/N CODE: Xk=[10,-2+i*2,-2,-2-i*2]

N=4 [Xn]=idft(Xk,N) yn=real(xn) stem(xn) title('IDFT of y(n)') OUTPUT:

APPLICATION OF DFT:

To find the frequency components of a signal buried in a noisy time domain signal. Consider data sampled at 1000 Hz. Form a signal containing 50 Hz and 120 Hz and corrupt it with some zero-mean random noise. CODE: t = 0:0.001:0.6; x = sin(2*pi*50*t)+sin(2*pi*120*t); y = x + 2*randn(size(t)); plot(1000*t(1:50),y(1:50)) title('Signal Corrupted with Zero-Mean Random Noise') xlabel('time (milliseconds)'); Y = fft(y,521); f = 1000*(0:256)/512; plot(f,abs(Y(1:257))); title('Frequency content of y'); xlabel('frequency (Hz)') OUTPUT:

CIRCULAR FOLDING: If g(t)G(w) then g(t-t0)G(w)e(-jwt0). Here time shift of t0 does not affect the magnitude spectrum. It augments the phase. CODE: n=0:10 x=10*(0.8).^n y=x(mod(-n,11)+1) subplot(2,1,1) stem(n,x) title('x(n)') subplot(2,1,2) stem(n,y) title('y(n)') X=fft(x,11) Y=fft(y,11) figure subplot(2,2,1) stem(n,real(X)) title('Real part of dft(X)') subplot(2,2,2) stem(n,real(Y)) title('Real part of dft(Y)') subplot(2,2,3) stem(n,imag(X)) title('Imaginary part of dft(X)') subplot(2,2,4)

stem(n,imag(Y)) title('Imaginary part of dft(Y)') OUTPUT:

CONCLUSION:______________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________ ____________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________ ____________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________ ____________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________

Lab-5 Application of DSP

Aim: To understand the application of DSP and study sound processing inMATLAB. Introduction: Digital signal processing has a varied applications.MATLAB is a tool with which we can perform various Digital Signal Processing. Also processing of sound signal is possible with MATLAB. Following applications of DSP are discussed below:

Generation of ECG signals without noise. Generation of ecg signal with noise. DTMF Sound processing

ECG without Noise: clear all; close all; cycle=zeros(1,500); y=[01 2 3 4 5 6 7 8 9 10 9 8 7 6 5 4 3 2 1 0]/4; t=[0:1:40]; a=(.05*(t-20).*(t-20)-20)/50; cycle(1:61)=2*[y a]; cyc=filter([1 1 1 1 1], [1], cycle); x=[cyc cyc cyc cyc cyc]; [idum, nsize]=size(x); t=[0:1:nsize-1]/500; plot(t,x);xlabel('Time(sec)');ylabel('Amplitude'); title('ECG signal');

ECG with noise: clear all; close all; x=-5:0.1:5; a=2; b=1; y=(-50+((a*x.*x)+(b*x)))/2; b=y; x=[0,1,2,3,4,5,6,7,8,9,8,7,6,5,4,3,2,1,0]; a=10*x; z=[a y 3+zeros(1,300)] c=z+randn(1,length(z)); k=[c c c]; s=[1,1,1,1]; t=[1,1]; q=filter(s,t,k); plot(q);

DTMF: clear all; clc; freq_row = [697 770 852 941]; freq_column = [1209 1336 1477]; r = 1; no = 1; for cell_no = 1:no c_no = input('press no.','s'); switch c_no case '*', row = 4; column = 1; case '0', row = 4; column = 2; case '#', row = 4; column = 3; otherwise, d_no = c_no - '0'; column = (mod((d_no - 1),3))+1; row = ((d_no - column)/3)+1; end freq_samp = 32768; t = 0:(1/freq_samp):0.25; sig1 = sin(2*pi*freq_row(row)*t); sig2 = sin(2*pi*freq_column(column)*t); sig = (sig1 + sig2)/2; sig3 = double(sig)/256; n = length(sig3); t1 = (0:n-1)/freq_samp; p = abs(fft(sig3)); f = (0:n-1)*(freq_samp/n); subplot(1,2,r); r = r+1; plot(f,p); axis([500 1700 0 50]); title('freq domain plot'); subplot(1,2,r); plot(t,sig); title('time domain plot'); r = r+1; end

CONCLUSION:__________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________ ____________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________ ____________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________

Lab 6 Filter Designing Analysis Tool

Aim: To study the Filter designing tool in MATLAB. Theory: The Filter Design and Analysis Tool (FDA Tool) is a powerful user interface for designing and analyzing filters quickly. FDA Tool enables you to design digital FIR or IIR filters by setting filter specifications, by importing filters from your MATLAB workspace, or by adding, moving or deleting poles and zeros. FDA Tool also provides tools for analyzing filters, such as magnitude and phase response and pole-zero plots. Design Filter: See Choosing a Filter Design Method for more information. You use this panel to Design filters from scratch. Modify existing filters designed in FDATool. Analyze filters. Import filter: See Importing a Filter Design for more information. You use this panel to Import previously saved filters or filter coefficients that you have stored in the MATLAB workspace, Analyze imported filters. Pole/Zero Editor. See Editing the Filter Using the pole/Zero Editor. You use this panel to add, delete, and move poles and zeros in your filter design. Following is the initial window of FDA tool.

Different Basic Windows used in Filtering: 1)Rectangular Window:

2)Triangular Window:

3)Hann Windowing:

4)Hamming Window:

5)Kiser Windowing:

Different kind of filtering using different windows: FIR: 1)Low pass Kiser windowing:

Impulse response:

Pole and zero plot

Group delay

2) Low pass Hamming windowing: Magnitude plot:

Impulse response:

Pole zero plot:

Group delay:

3)High pass Triangular windowing: Magnitude plot:

Impulse response:

Pole zero plot: Group delay:

4)High pass Hann windowing: Magnitude response:

Impulse response:

Pole zero plot:

Group delay:

5) Pass band Rectangular windowing: Magnitude response:

Impulse response:

Pole zero plot:

Group delay:

6) Stop band Rectangular windowing: Magnitude response:

Impulse response:

Pole zero plot:

Group delay:

IIR: 1)Butterworth Low pass: Magnitude plot

Impulse response:

Pole zero plot:

Group delay:

2)Butterworth High pass: Magnitude plot

Impulse response:

Pole zero plot:

Group delay:

CONCLUSION:__________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________ ____________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________

LAB 7 Designing filter with given specifications

Aim: Design and implement an analog filter with given specification. Introduction: Analog filters are indispensable in many situations. The front end of most digital signal processing devices is an anti aliasing analog filter that limits the input signal to a range of frequencies that the digital filter can handle. The design of classical analog filters is based on approximating the magnitude or phase specification by polynomials or rational functions. Of the four classical filter types based on magnitude specifications, the Butterworth filter is monotonic in the pass band and stop band, the Chebyshev I filter displays ripples in the pass band but is monotonic in the stop band, the Chebyshev II filter displays ripples in the stop band but is monotonic in the pass band, and the elliptical filter has ripples in both bands. Butterworth highpass Filter clc; clear all; close all; Fs= 0.4000; Fp= 0.5000; As= 80; Ap= 1; hs = fdesign.highpass(Fs,Fp,As,Ap); butter(hs);

Butterworth lowpass filter clc; clear all; close all; Fp= 0.4; Fs= 0.5; Ap= 1; As= 80; hs = fdesign.lowpass(Fp,Fs,Ap,As); butter(hs);

Butterworth bandpass filter clc; clear all; close all; Fp1= 0.25; Fst1= 0.35; Fst2= 0.65; Fp2= 0.75; Ap1= 1; Ast= 60; Ap2= 1; hs = fdesign.bandstop(Fp1,Fst1,Fst2,Fp2,Ap1,Ast,Ap2); butter(hs);

Butterworth bandstop filter clc; clear all; close all; Fp1= 0.25; Fst1= 0.35; Fst2= 0.65; Fp2= 0.75; Ap1= 1; Ast= 60; Ap2= 1; hs = fdesign.bandstop(Fp1,Fst1,Fst2,Fp2,Ap1,Ast,Ap2); butter(hs);

Chebyshev highpass filter clc; clear all; close all; Fs= 0.4000; Fp= 0.5000; As= 80; Ap= 1; hs = fdesign.highpass(Fs,Fp,As,Ap); cheby2(hs);

Chebyshev lowpass filter clc; clear all; close all; Fp= 0.4; Fs= 0.5; Ap= 1; As= 80; hs = fdesign.lowpass(Fp,Fs,Ap,As); cheby2(hs);

Chebyshev bandpass filter clc; clear all; close all; Fst1= 0.35; Fp1= 0.45; Fp2= 0.55; Fst2= 0.65; Ast1=60; Ap= 1; Ast2= 60; hs = fdesign.bandpass(Fst1,Fp1,Fp2,Fst2,Ast1,Ap,Ast2); cheby2(hs);

Chebyshev bandstop filter clc; clear all; close all; Fp1= 0.25; Fst1= 0.35; Fst2= 0.65; Fp2= 0.75; Ap1= 1; Ast= 60; Ap2= 1; hs = fdesign.bandstop(Fp1,Fst1,Fst2,Fp2,Ap1,Ast,Ap2); cheby2(hs);

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Assignment 1 Plot the frequency response for 10th order lowpass butterworth filter having cutoff frequency 1khz. clc; clear all; close all; n=10; Fp=1000/10000; [z,p,k]=butter(n,Fp,'low'); [sos,g] = zp2sos(z,p,k); Hd = dfilt.df2tsos(sos,g); h = fvtool(Hd); set(h,'Analysis','freq')

Assignment 2 Design lowpass butterworth digital filter for given specification fp=0.4 fs=0.5 Ap=1db As=80 db clc; clear all; close all; Ap=1; As=80; wp=0.4; ws=0.5; [n,wn]=buttord(wp,ws,Ap,As); [z,p,k]=butter(n,wn,'low'); [sos,g] = zp2sos(z,p,k); Hd = dfilt.df2tsos(sos,g); h = fvtool(Hd); set(h,'Analysis','freq');

Assignment 3 Design bandpass butterworth digital filter f1=0.35 f2=0.45 f3=0.55 f4=0.65 Ap=1db

As=60 db on both sides clc; clear all; close all; Ws=[0.35 0.65]; Wp=[0.45 0.55]; Ap=1; As=60; [n,wn]=buttord(Wp,Ws,Ap,As); [z,p,k]=butter(n,wn,'bandpass'); [sos,g] = zp2sos(z,p,k); Hd = dfilt.df2tsos(sos,g); h = fvtool(Hd); set(h,'Analysis','freq');

Lab 8 Code Composer Studio

Aim: Introduction to CCStudio and study of T16x architecture through CCS help. a. To create a simple project using C file. b. Perform simulator feature viewing memory and graph. c. To study T16x CPU architecture through help. METHOD: A. To create a simple project using .C file. Set up CCS for simulator C67xx CPU

Open CCS Create a project: Choose project - new - Type the project name say exp1; location to be \ti6x\myprojects; project type .out; target 67xx

Create a .c source file: Using a CCS editor, choose, file - new - source file write a simple .C program to print a message Welcome to learning TI6x.

Create a .cmd command file: These are basically a system command files which informs the CCS about memory configuration i.e. the defined segments in a project have to be loaded into which part of the memory, whether internal, external, serial etc.

Adding files to the project: We need to combine the created files as a project. For that choose project -add files to the project- select the files to be added lab1a.c and ..ti6x\tutorials\hello1\hello.cmd. We also need to add a library file from \ti6x\C6000\cgtools\lib\rts6700.lib

Building project: Building is compiling + linking, which creates executable .out file. Only compiling or linking options are also available. Choose project - build Before building a project we can set proper building options for linker, compiler and optimization level. Here, .c file is converted into the equivalent machine code for 67x processor Higher level to lower level conversion requires optimization. Loading the program: Now we can load the program into the DSP memory choosing file - load program options.

Running a file: Select Debug - run to run the program and see the result on stdout window. B. To add .asm file to a project. create a new .asm file which contains a data to display a sine wave. As we are not to call this .asm file in the main program, it should be a data file.

Add this file to the project.

Make a change in the .cmd file. The data is defined in the mydata section (a user defined name). So, we need to declare in the .cmd file that this section should be written in SDRAM area.

Build and load the project as before. If there is error in each line, upon building, the reason may be an assembly code requires that all of the lines in the file not start from the first column. Verify this and modify accordingly. By double clicking on the error message will open the file and point the cursor at the line where the error is.

Run it. The output is displayed. C. To view data in memory and through graph. Select view - memory Select the memory location. In our case it is 0x80000000 Format: 16-bit signed int A memory window appears where the results can be obtained.

Select view - graph - time/frequency start address: 0x80000000 Acquisition buffer size: 10 Display data size: 10 DSP data size: 16-bit signed integer A graph will appear on the screen with a plot of value v/s element number Program to print HELLO on screen #include void main() { printf(HELLO); } Output: HELLO

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LAB 9

Higher level language Program in CCS

AIM: Add assembly file to project through CCS. a. To add assembly file to a project. PROGRAM STATEMENT: c. Write a program to display a message Welcome to the world of TI6x using .c program. d. Add .asm file, which contains a table with 10 values to generate a sine wave. REQUIREMENTS: CCStudio for 6x METHOD: A. To add .asm file to a project. i. create a new .asm file which contains a data to display a sine wave. As we are not to call this .asm file in the main program, it should be a data file. ii. Add this file to the project. iii. Make a change in the .cmd file. The data is defined in the mydata section (a user defined name). So, we need to declare in the .cmd file that this section should be written in SDRAM area. iv. Build and load the project as before. If there is error in each line, upon building, the reason may be an assembly code requires that all of the lines in the file not start from the first column. Verify this and modify accordingly. By double clicking on the error message will open the file and point the cursor at the line where the error is. v. Run it. The output is displayed.

Sample program Write a program to generate Full Wave Sinusoidal signal in CCS. #include main() { float *p=(float*)0x80000000,pi=3.14; int f=100,t; for(t=0;t

Assignment-1 Write a program to generate Full wave rectified sinusoidal signal in CCS. #include main() { float *p=(float*)0x80000000,pi=3.14; int f=100,t; for(t=0;t

Assignment-2 Write a program to generate half wave rectified sinusoidal signal in CCS. #include main() { float *p=(float*)0x80000000,pi=3.14; int f=100,t; for(t=0;t

LAB 10

Programming Techniques in CCS

AIM: To Perform different programming techniques. i. C calling assembly function ii. C calling linear assembly function. PROGRAM STATEMENT: a. Write a .C program, calling .asm function to multiply two arrays. b. Write a .C program to Find Factorial of entered No. using C calling ASM function(assembly REQUIREMENTS: CCStudio for 6x THEORY about PROGRAMMING TI 6713: Options: To program TI 6713 there are so many alternative options 1. Only C/C++: CCS project can have only .c source file 2. Assembly: CCS project can have only .asm source file 3. Linear Assembly: CCS project can have only .sa linear assembly source file 4. Mixed: CCS project can have .c calling .asm ; .c calling .sa; .asm calling .asm 1. Programming through .c Usual .c syntax Program written using higher level is converted into an assembly level program. Over the last couple of years, C compiler optimizer has become more and more efficient. Although C code is less efficient (speed performance) than assembly level program, it typically involves less coding effort than assembly code which can be hand optimized to achieve a 100 percent efficiency but with much better coding effort. 2. Assembly Level Programming: A. Instructions: The format of the assembly code line is: lable parallel bar [condition] instruction unit operands ;comments An assembly must contain only the label in the first column. So other than labels, directives or mnemonics can not start from the first column. B. Assembler directives: The smallest unit of the object file is called section. A section is a block of code or data that occupies space in the memory map with other sections. There are two basic types of sections. Initialized sections: They contain code or data. E.g. .text, .data, .sect Uninitialized sections: They reserve space in the memory map for uninitialized data. It is a place for creating and storing variables during execution. E.g. .bss, .usect Object files always contain three default sections: .text, .data, .bss .text To declare the following section as program .data To declare the following section as data .sect name defines a section of code or data named name and associates subsequent code or data with that section. Ex. mydata .bss symbol, size in bytes reserves space for uninitialized variables . Other directives are: Directive that reference or define files .def ; .ref; .global Directives that initialize constants (data and memory) .byte; .short; .float, .int , .double Directives that define symbols at assembly time .equ; .set 3. Linear Assembly Programming: A. General Programming: In linear assembly programming, syntax of assembly code instructions: ADD, SUB is utilized. But the operands operands are written as in C. Variables are used to designate the registers. Say, MPY a, b, prod ADD prod, sum, sum Parallel instructions are not valid in a linear assembly program. Specifying the functional unit, register or NOPs is optional. B. An alternative to .c or .asm is linear assembly .sa program. An assembler optimizer is used with linear assembly program to create a .asm program, in much the same way as C compiler is used with .c to .asm conversion. The assembler optimizer gives better performance than the c compiler optimizer. In short, linear assembly code programming provides a compromise between a coding effort and coding efficiency. C. Assembler Directives: .cproc and .endproc: .cproc is the directive to declare the following section of code as C callable linear assembly function. It is used always with .endproc, which indicates end of the linear assembly function. Syntex: label ,cproc a, b, prod Where, the label is a name of the procedure and a, b, prod are the variables being passed. All the input and output parameters being passed must be written after .cproc as we pass parameter to a C functions. .proc and .endproc: They are used to define a general procedure start and end.

The .cproc directive differs from the .proc directive in that the compiler treats the .cproc region as a C/C++ callable function. The assembly optimizer performs some operations automatically in a .cproc region in order to make the function conform to the C/C++ calling conventions and to C/C++ register usage conventions. .return This directive is used to return result of calling function. .reg - The .reg directive allows you to use descriptive names for values that are stored in registers. The assembly optimizer chooses a register for you such that its use agrees with the functional units chosen for the instructions that operate on the value. .reg ahi:alo ADD a0,ahi:alo,ahi:alo .def defines a function 4. Mixed programming mode: The mixed programming mode is possible where c program can call assembly or linear assembly functions or assembly program calls an assembly level procedure. C calling assembly: Declaration and call reference: In the c program, calling an assembly function syntax is same as we are calling a c function. An external declaration of an assembly function called within a c program using extern directive is optional. Say, extern short dotp_assem_func() : : result = dotp_assem_func(ap, bp); The function name should be defined in .asm using .def directive and the function name should be preceded by an undescore, which indicates that this is a c callable .asm function. I.e. .def _dotp_assem_func The name of the *.c file should not be same as the *.asm file. Parameter passing: The parameters passed through the C program are in terms of variables. These parameters are available as input parameters to an assembly level procedure through registers A4, B4, A6, B6, A8, sequentially. Same way, the output parameter is always through A4. (Only one retur parameter as per C notation.) The return address has to be passed through B3. C calling linear assembly: Enough has been discussed before Assembly calling assembly: The calling assembly program has to be defined as init and correspondingly the first assembly code line should be given a label init. Other declaration and parameter passing is in the same way. System Initialization: After reset from where the processor should start the execution that has to be informed. Processor executes an interrupt service routine (ISR) for reset interrupt, where we can write a code to branch (jump) to the our required program. If there is a .c program in our project, before we can run a C/C++ program, other than this initialization, we must create the C/C++ run-time environment. The C/C++ boot routine performs this task using a function called c_int00. The run-time-support source library, rts.src, contains the source to this routine in a module called boot.c. That is why, if our project contains .c program then it is compulsory to add to the project the run time support source library file. If there is no .c program in our project, then to inform the processor to start the program execution after reset from our defined init procedure, we have to add a vector.asm file to our project, which basically is an reset interrupt service routine (ISR) to make a branch (jump) to the required location. If there is no .c program in our project, before building the project, we must modify the linker option (Project Options) to select No Autoinitializtion. Otherwise, the warning entry point symbol _c_int00 undefined is displayed on building this project with no main() function ion C. PROGRAM: //Dotp4clasm.c Multiplies two arrays using C calling linear ASM func short dotp_lasm_func(short *a,short *b,short ncount); //prototype #include //for printing statement //#include "dotp4.h" //arrays of data values #define count 4 //number of data values short x[count] = {1,4,2,6}; //declare 1st array short y[count] = {6,9,5,8}; //declare 2nd array int result = 0; //result main() { result = dotp_lasm_func(x,y,count); //call linear ASM func printf("result = %d decimal \n", result); //print result } Dotp4afunc.asm Multiply two arrays. Called from dotp4a_init.asm ;A4=x address,B4=y address,A6=count(size of array),B3=return address .def dotp_lasm_func; dot product function .text ; text section dotp_lasm_function MOV A6,A1 ;move loop count -->A1 ZERO A7 ; init A7 for accumulation loop LDH *A4++,A2 ;A2=(x. A4 as address pointer LDH *B4++,B2 ;B2=(y). B4 as address pointer NOP 4 ;4 delay slots for LDH MPY .M1x B2,A2,A3 ;A3 = x * y NOP ;1 delay slot for MPY ADD A3,A7,A7 ;sum of products in A7 SUB A1,1,A1 ;decrement loop counter [A1] B loop ;branch back to loop till A1=0 NOP 5 ;5 delay slots for branch

MOV A7,A4 ;A4=result A4=return register B B3 ;return from func to addr in B3 NOP 5 ;5 delay slots for branch PROGRAM: Find Factorial of entered No. using C calling ASM function(assembly) //Factorial.c Finds factorial of n. Calls function factfunc.asm #include //for print statement extern short fact_func(short); void main() { short n=5; //set value int result; //result from asm function scanf("%d",&n); //if (n!=0) result = fact_func(n); //call assembly function factfunc //else result=1; printf("no. = %d \n factorial = %d\n",n,result); //print result from asm function } OUTPUT WATCH WINDOW:

;FACT_FUNC.ASM Assembly function called from C to find factorial .def _fact_func ;asm function called from C _fact_func: MV A4,A1 ;setup loop count in A1 ZERO A4 ADD A4,1,A4 [A1] B LOOP NOP 5 B B3 NOP 5 LOOP: MPY A4,A1,A4 ;accumulate in A4 NOP ;for 1 delay slot with MPY SUB A1,1,A1 ;decrement for next multiply [A1] B LOOP ;branch to LOOP if A1 # 0 NOP 5 ;five NOPs for delay slots B B3 ;return to calling routine NOP 5 ;five NOPs for delay slots END INPUT INPUT VALUE: 5 OUTPUT FACTORIAL= 120 CONCLUSION:__________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________ ____________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________ ____________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________

Lab 11

DSK 6713 KIT

AIM : Introduction to DSK 6713 Kit

PROCEDURE:

1) Open 6713 DSK Diagnostics 2) Press start if kit is connected properly it will show PASS otherwise FAIL 3) Then open ccstudio setup. 4) Available factory board-family-platform-select C6173 DSK C67xx-dsk 5) Save and quit 6) Open project

-Built -Connect -Rebuilt -Load -Run

7) Then see the Output on the kit

PROGRAM:

/*

* Copyright 2003 by Spectrum Digital Incorporated.

* All rights reserved. Property of Spectrum Digital Incorporated.

*/

/*

* ======== led.c ========

*

* This example blinks LED #0 at a rate of about 2.5 times per second using

* the LED module of the the DSK6713 Board Support Library. The example

* also reads the state of DIP switch #3 and lights LED #3 if the switch

* is depressed or turns it off if the switch is not depressed.

*

* The purpose of this example is to demonstrate basic BSL usage as well

* as provide a project base for your own code.

*

* Please see the DSK6713 help file for more detailed information.

*/

/*

* DSP/BIOS is configured using the DSP/BIOS configuration tool. Settings

* for this example are stored in a configuration file called led.cdb. At

* compile time, Code Composer will auto-generate DSP/BIOS related files

* based on these settings. A header file called ledcfg.h contains the

* results of the autogeneration and must be included for proper operation.

* The name of the file is taken from led.cdb and adding cfg.h.

*/

#include "ledcfg.h"

/*

* The Board Support Library is divided into several modules, each

* of which has its own include file. The file dsk6713.h must be included

* in every program that uses the BSL. This example also includes

* dsk6713_led.h and dsk6713_dip.h because it uses the LED and DIP modules.

*/

#include "dsk6713.h"

#include "dsk6713_led.h"

#include "dsk6713_dip.h"

/*

* main() - Main code routine, initializes BSL and runs LED application

*/

/*

* EXTRA: Pressing DIP switch #3 changes LED #3 from off to on.

*/

void main()

{

/* Initialize the board support library, must be first BSL call */

DSK6713_init();

/* Initialize the LED and DIP switch modules of the BSL */

DSK6713_LED_init();

DSK6713_DIP_init();

while(1)

{

/* Toggle LED #0 */

DSK6713_LED_toggle(0);

/* Check DIP switch #3 and light LED #3 accordingly, 0 = switch pressed */

if (DSK6713_DIP_get(2) == 0)

/* Switch pressed, turn LED #3 on */

DSK6713_LED_on(2);

else

/* Switch not pressed, turn LED #3 off */

DSK6713_LED_off(2);

/* Spin in a software delay loop for about 200ms */

DSK6713_waitusec(200000);

}

}

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