CONTENTS

DSP Lab Manual

LAB MANUAL

DIGITAL SIGNAL PROCESSING LABORATORY

III Year B. Tech. ECE-II SEM

DEPARTMENT OF ELECTRONICS AND COMMUNICATION ENGINEERING

VIGNANS INSTITUTE OF TECHNOLOGY & AERONAUTICAL ENGINEERINGDESHMUKHI(V),VIGNAN HILLS, NALGONDA Dist-508284.

PART-A:INRODUCTIONMATLAB: MATLAB is a software package for high performance numerical computation and visualization provides an interactive environment with hundreds of built in functions for technical computation, graphics and animation. The MATLAB name stands for MATrix Laboratory The diagram shows the main features and capabilities of MATLAB.ToolboxSignal processing Image processingStatisticsControl systemNeural networksCommunicationsComputationsLinear algebraSignal processingPolynomials & interpolationQuadratureSolution of ODEs

MATLABExternal interfaceInterface with C and Fortran programsGraphics2-D graphics3-D graphicsAnimation

At its core ,MATLAB is essentially a set (a toolbox) of routines (called m files or mex files) that sit on your computer and a window that allows you to create new variables with names (e.g. voltage and time) and process those variables with any of those routines (e.g. plot voltage against time, find the largest voltage, etc).

It also allows you to put a list of your processing requests together in a file and save that combined list with a name so that you can run all of those commands in the same order at some later time. Furthermore, it allows you to run such lists of commands such that you pass in data and/or get data back out (i.e. the list of commands is like a function in most programming languages). Once you save a function, it becomes part of your toolbox (i.e. it now looks to you as if it were part of the basic toolbox that you started with).

For those with computer programming backgrounds: Note that MATLAB runs as an interpretive language (like the old BASIC). That is, it does not need to be compiled. It simply reads through each line of the function, executes it, and then goes on to the next line. (In practice, a form of compilation occurs when you first run a function, so that it can run faster the next time you run it.)

MATLAB Windows:MATLAB works with through three basic windows Command Window : This is the main window .it is characterized by MATLAB command prompt >> when you launch the application program MATLAB puts you in this window all commands including those for user-written programs ,are typed in this window at the MATLAB prompt

Graphics window: the output of all graphics commands typed in the command window are flushed to the graphics or figure window, a separate gray window with white background color the user can create as many windows as the system memory will allow

Edit window: This is where you write edit, create and save your own programs in files called M files.Input-output:MATLAB supports interactive computation taking the input from the screen and flushing, the output to the screen. In addition it can read input files and write output filesData Type: the fundamental data type in MATLAB is the array. It encompasses several distinct data objects- integers, real numbers, matrices, charcter strings, structures and cells.There is no need to declare variables as real or complex, MATLAB automatically sets the variable to be real.Dimensioning: Dimensioning is automatic in MATLAB. No dimension statements are required for vectors or arrays .we can find the dimensions of an existing matrix or a vector with the size and length commands.

Basic Instructions in Mat lab1. T = 0: 1:10 This instruction indicates a vector T which as initial value 0 and final value 10 with an increment of 1Therefore T = [0 1 2 3 4 5 6 7 8 9 10]

2. F= 20: 1: 100 Therefore F = [20 21 22 23 24 100]3. T= 0:1/pi: 1 Therefore T= [0, 0.3183, 0.6366, 0.9549]4. zeros (1, 3)The above instruction creates a vector of one row and three columns whose values are zero Output= [0 0 0]5. zeros( 2,4)Output = 0 0 0 0 0 0 0 0 6. ones (5,2)The above instruction creates a vector of five rows and two columns

Output = 1 11 11 11 11 17. a = [ 1 2 3] b = [4 5 6] a.*b = [4 10 18]Which is multiplication of individual elements? i.e. [4X1 5X2 6X3]8 if C= [2 2 2]b.*C results in [8 10 12]9. plot (t, x) Ifx = [6 7 8 9]t = [1 2 3 4]

This instruction will display a figure window which indicates the plot of x versus t

9 8

7 6

1 2`34 10. stem (t,x)

This instruction will display a figure window as shown 9 8 7 6

11. Subplot: This function divides the figure window into rows and columnsSubplot (2 2 1) divides the figure window into 2 rows and 2 columns 1 represent number of the figure

1 2 (2 2 1) (2,2,2)

34 (2 2 3) ( 2 2 4)

Subplot(3, 1, 3)1 (3, 1, 1)

2 (3, 1, 2)

3 (3, 1, 3)

12 Filter Syntax: y = filter(b,a,X)Description: y = filter(b,a,X) filters the data in vector X with the filter described by numerator coefficient vector b and denominator coefficient vector a.If a(1) is not equal to 1, filter normalizes the filter coefficients by a(1). If a(1) equals 0, filter returns an error.

13. Impz Syntax: [h,t] = impz(b,a,n) Description: [h,t] = impz(b,a,n) computes the impulse response of the filter with numerator coefficients b and denominator coefficients a and computes n samples of the impulse response when n is an integer (t = [0:n-1]'). If n is a vector of integers, impz computes the impulse response at those integer locations, starting the response computation from 0 (and t = n or t = [0 n]).If, instead of n, you include the empty vector [] for the second argument, the number of samples is computed automatically by default.14. FliplrSyntax: B = fliplr(A)Description: B = fliplr(A) returns A with columns flipped in the left-right direction, that is, about a vertical axis.If A is a row vector, then fliplr(A) returns a vector of the same length with the order of its elements reversed. If A is a column vector, then fliplr(A) simply returns A.15. ConvSyntax: w = conv(u,v)Description: w = conv(u,v) convolves vectors u and v. Algebraically, convolution is the same operation as multiplying the polynomials whose coefficients are the elements of u and v.

16.DispSyntax: disp(X)Description: disp(X) displays an array, without printing the array name. If X contains a text string, the string is displayed.Another way to display an array on the screen is to type its name, but this prints a leading "X=," which is not always desirable.Note that disp does not display empty arrays.

17.xlabelSyntax: xlabel('string')Description: xlabel('string') labels the x-axis of the current axes.

18. ylabelSyntax : ylabel('string')Description: ylabel('string') labels the y-axis of the current axes.

19.Title Syntax : title('string')Description: title('string') outputs the string at the top and in the center of the current axes.

20.grid onSyntax : grid onDescription: grid on adds major grid lines to the current axes.

Experiment 1: To find DFT of given sequence

Aim: To find DFT of given sequence

Theory: DescriptionFourier analysis is a family of mathematical techniques, all based on ecomposing signals into sinusoids. The discrete Fourier transform (DFT) is the family member used with digitized signals. A signal can be either continuous or discrete, and it can be either periodic or Aperiodic. The combination of these two features generates the four categories, described below Aperiodic-ContinuousThis includes, decaying exponentials and the Gaussian curve. These signals extend to both positive and negative infinity without repeating in a periodic pattern. The Fourier Transform for this type of signal is simply called the Fourier Transform. Periodic-ContinuousThis includes: sine waves, square waves, and any waveform that repeats itself in a regular pattern from negative to positive infinity. This version of the Fourier transform is called the Fourier series. Aperiodic-DiscreteThese signals are only defined at discrete points between positive and negative infinity,and do not repeat themselves in a periodic fashion. This type of Fourier transform is called the Discrete Time Fourier Transform. Periodic-DiscreteThese are discrete signals that repeat themselves in a periodic fashion from negative to positive infinity. This class of Fourier Transform is sometimes called the Discrete Fourier Series, but is most often called the Discrete Fourier Transform.

Discrete Fourier Transform Computation:Mathematical Expression to calculate DFT for an input sequence x(n)

Matlab Codeclf;a=[1 1 2 2];x=fft(a,4);n=0:3;subplot(2,1,1);stem(n,abs(x));xlabel('time index n');ylabel('Amplitude');title('amplitude obtained by dft');grid;subplot(2,1,2);stem(n,angle(x));xlabel('time index n'); ylabel('Amplitude');title('Phase obtained by dft');

Experiment No 2. To find frequency response of LTI system given in transfer function / difference equation

Aim: To find frequency response of LTI system given in transfer function / difference equation

% Frequency Response of LTI system using difference equation

clf;num=input('numerator coefficiuents b = ');den=input('denominator coefficients a = ');w = -pi:8*pi/511:pi;h = freqz(num, den, w);% Plot the DTFTsubplot(2,1,1)plot(w/pi,abs(h));gridtitle('Magnitude of H')xlabel('\omega /\pi');ylabel('Amplitude');subplot(2,1,2)plot(w/pi,phase(h));gridtitle('phase of H')xlabel('\omega /\pi');ylabel('phase');

Output

freqrespnumerator coefficiuents b = [2 1]denominator coefficients a = [1 -0.6]

EXPERIMENT NO 3: Impulse response of a given system

Aim: To find the impulse response h(n) of the given LTI system whose response y(n) to an input x(n) is given.

Theory: Fig.2.1 A LTI system

A discrete time LTI system (also called digital filters) as shown in Fig.2.1 is represented by A linear constant coefficient difference equation, for example, A system function H(z) (obtained by applying Z transform to the difference equation). Given the difference equation or H(z), the impulse response of the LTI system is found using filter or impz MATLAB functions.

MATLAB Programs:Program 1

b=input('numerator coefficiuents b = ');a=input('denominator coefficients a = ');n=input('enter length n = ');[h,t] = impz(b,a,n);disp(h);stem(t,h);xlabel('samples n ' ,'color' , 'm');ylabel('amplitude h(n)','color','m');title('impulse response','color','r');grid on;

Result:numerator coefficiuents b = [1]denominator coefficients a = [1 -0.5]enter length n = 20 1.0000 0.5000 0.2500 0.1250 0.0625 0.0313 0.0156 0.0078 0.0039 0.0020 0.0010 0.0005 0.0002 0.0001 0.0001 0.0000 0.0000 0.0000 0.0000 0.0000

Experiment No. 4 Implementation of N point FFT

Aim: To Implement FFT algorithm of a given sequence and to plot magnitude and phase spectra

Theory: Discrete Fourier Transform (DFT) is used for performing frequency analysis of discrete time signals. DFT gives a discrete frequency domain representation whereas the other transforms are continuous in frequency domain. The N point DFT of discrete time signal x[n] is given by the equation

Where N is chosen such that , where L=length of x[n]. The inverse DFT allows us to recover the sequence x[n] from the frequency samples.

X(k) is a complex number (remember ejw=cosw + jsinw). It has both magnitude and phase which are plotted versus k. These plots are magnitude and phase spectrum of x[n]. The k gives us the frequency information. Here k=N in the frequency domain corresponds to sampling frequency (fs). Increasing N, increases the frequency resolution, i.e., it improves the spectral characteristics of the sequence. For example if fs=8kHz and N=8 point DFT, then in the resulting spectrum, k=1 corresponds to 1kHz frequency. For the same fs and x[n], if N=80 point DFT is computed, then in the resulting spectrum, k=1 corresponds to 100Hz frequency. Hence, the resolution in frequency is increased. Since , increasing N to 8 from 80 for the same x[n] implies x[n] is still the same sequence (length of the sequence).3. Implement FFT algorithm.4. Plot the magnitude & phase spectra.

MATLAB Program:

% This programme is for fast fourier transform in matlab.% Where y is the input argument and N is the legth of FFT . Let % y = [1 2 3 4 ]; y=input('enter input sequence:');N= input('size of fft(e.g . 2,4,8,16,32):'); n=length(y); p=log2(N); Y=y; Y=[Y, zeros(1, N - n)]; N2=N/2; YY = -pi*sqrt(-1)/N2; WW = exp(YY); JJ = 0 : N2-1; W=WW.^JJ; for L = 1 : p-1 u=Y(:,1:N2); v=Y(:,N2+1:N); t=u+v; S=W.*(u-v); Y=[t ; S]; U=W(:,1:2:N2); W=[U ;U]; N=N2; N2=N2/2; end; u=Y(:,1); v=Y(:,2); Y=[u+v;u-v]; Ysubplot(3,1,1)stem(y);gridtitle('input sequence y')xlabel('n');ylabel('Amplitude');subplot(3,1,2) stem(abs(Y));gridtitle('Magnitude of FFT')xlabel('N');ylabel('Amplitude');subplot (3,1,3)stem(angle(Y));gridtitle('phase of FFT')xlabel('N');ylabel('phase');

Result:>> fft1enter input sequence:[1 2 3 4 5 6 7]size of fft(e.g . 2,4,8,16,32):8Y = 28.0000 -9.6569 + 4.0000i -4.0000 - 4.0000i 1.6569 - 4.0000i 4.0000 1.6569 + 4.0000i -4.0000 + 4.0000i -9.6569 - 4.0000i

Experiment No. 5 Power Density Spectrum Using MATLAB

Write a MATLAB program to compute Power Density Spectrum of a given signal

Algorithm:1. Generate a signal 2. find DFT of the signal3. PSD is given by the formula

y(n) = where n = - (N-1) to (N-1)PSD=|Y(w)|2 Where Y(w) = fft of y(n)

Matlab program % computation of psd of signal corrupted with random noiset = 0:0.001:0.6;x = sin(2*pi*50*t)+sin(2*pi*120*t);y = x + 2*randn(size(t));figure,plot(1000*t(1:50),y(1:50))title('Signal Corrupted with Zero-Mean Random Noise')xlabel('time (milliseconds)');Y = fft(y,512);%The power spectral density, a measurement of the energy at various frequencies, is:Pyy = Y.* conj(Y) / 512;f = 1000*(0:256)/512;figure,plot(f,Pyy(1:257))title('power spectrum of y');xlabel('frequency (Hz)');

Output

Experiment No. 6 and 7 Design and implementation of FIR filter to meet given specifications

Aim: To design and implement a FIR filter for given specifications.

Theory:There are two types of systems Digital filters (perform signal filtering in time domain) and spectrum analyzers (provide signal representation in the frequency domain). The design of a digital filter is carried out in 3 steps- specifications, approximations and implementation.

DESIGNING AN FIR FILTER (using window method):

Method I: Given the order N, cutoff frequency fc, sampling frequency fs and the window. Step 1: Compute the digital cut-off frequency Wc (in the range - < Wc < , with corresponding to fs/2) for fc and fs in Hz. For example let fc=400Hz, fs=8000HzWc = 2** fc / fs = 2* * 400/8000 = 0.1* radiansFor MATLAB the Normalized cut-off frequency is in the range 0 and 1, where 1 corresponds to fs/2 (i.e.,fmax)). Hence to use the MATLAB commandswc = fc / (fs/2) = 400/(8000/2) = 0.1Note: if the cut off frequency is in radians then the normalized frequency is computed as wc = Wc / Step 2: Compute the Impulse Response h(n) of the required FIR filter using the given Window type and the response type (lowpass, bandpass, etc). For example given a rectangular window, order N=20, and a high pass response, the coefficients (i.e., h[n] samples) of the filter are computed using the MATLAB inbuilt command fir1 as h =fir1(N, wc , 'high', boxcar(N+1));Note: In theory we would have calculated h[n]=hd[n]w[n], where hd[n] is the desired impulse response (low pass/ high pass,etc given by the sinc function) and w[n] is the window coefficients. We can also plot the window shape as stem(boxcar(N)). Plot the frequency response of the designed filter h(n) using the freqz function and observe the type of response (lowpass / highpass /bandpass).

Method 2: Given the pass band (wp in radians) and Stop band edge (ws in radians) frequencies, Pass band ripple Rp and stopband attenuation As. Step 1: Select the window depending on the stopband attenuation required. Generally if As>40 dB, choose Hamming window. (Refer table ) Step 2: Compute the order N based on the edge frequencies asTransition bandwidth = tb=ws-wp;N=ceil (6.6*pi/tb);1. Step 3: Compute the digital cut-off frequency Wc asWc=(wp+ws)/2 Now compute the normalized frequency in the range 0 to 1 for MATLAB aswc=Wc/pi;Note: In step 2 if frequencies are in Hz, then obtain radian frequencies (for computation of tb and N) as wp=2*pi*fp/fs, ws=2*pi*fstop/fs, where fp, fstop and fs are the passband, stop band and sampling frequencies in Hz Step 4: Compute the Impulse Response h(n) of the required FIR filter using N, selected window, type of response(low/high, etc) using fir1 as in step 2 of method 1.

MATLAB IMPLEMENTATION FIR1 Function : 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]. If Wn is a multi-element vector, Wn = [W1 W2 W3 W4 W5 ... WN], FIR1 returns an order N multiband filter with bands 0 < W < W1, W1 < W < W2, ..., WN < W < 1.FREQZ Digital filter frequency response. [H,W] = FREQZ(B,A,N) returns the N-point complex frequency response vector H and the N-point frequency vector W in radians/sample of the filter whose numerator and denominator coefficients are in vectors B and A. The frequency response is evaluated at N points equally spaced around the upper half of the unit circle. If N isn't specified, it defaults to 512.For FIR filter enter A=1 and B = h[n] coefficients. Appropriately choose N as 128, 256, etc

Window Transition Width Min. Stop band Matlab Name ApproximateExact values Attenuation Command

Rectangular 21db B= FIR1(N,Wn,boxcar)

Bartlett 25db B = FIR1(N,Wn,bartlett)

Hanning 44db B = FIR1(N,Wn,hanning)

Hamming 53db B= FIR1(N,Wn,hamming)

Blackman 74db B = FIR1(N,Wn,blackman)

Program:

%Method 2: the following program gives only the design of the FIR filter- for implementation continue with the next program (after h[n])%input data to be given: Passband & Stopband frequency% Data given: Passband ripple & stopband attenuation As. If As>40 dB, Choose hamming

clearwpa=input('Enter passband edge frequency in Hz');wsa= input('Enter stopband edge frequency in Hz');ws1= input('Enter sampling frequency in Hz');%Calculate transmission BW,Transition band tb,order of the filterwpd=2*pi*wpa/ws1;wsd=2*pi*wsa/ws1;tb=wsd-wpd;N=ceil(6.6*pi/tb)wc=(wsd+wpd)/2;%compute the normalized cut off frequencywc=wc/pi;%calculate & plot the windowhw=hamming(N+1);stem(hw);title('Fir filter window sequence- hamming window');%find h(n) using FIRh=fir1(N,wc,hamming(N+1));%plot the frequency responsefigure(2);[m,w]=freqz(h,1,128);mag=20*log10(abs(m));plot(ws1*w/(2*pi),mag);title('Fir filter frequency response');grid;

%generate simulated input of 50, 300 & 200 Hz, each of 30 pointsn=1:30;f1=50;f2=300;f3=200;fs=1000;x=[];x1=sin(2*pi*n*f1/fs);x2=sin(2*pi*n*f2/fs);x3=sin(2*pi*n*f3/fs);x=[x1 x2 x3];subplot(2,1,1);stem(x);title('input');%generate o/p%y=conv(h,x);y=filter(h,1,x);subplot(2,1,2);stem(y);title('output');

Result:Enter passband edge frequency in Hz100Enter stopband edge frequency in Hz200Enter sampling frequency in Hz1000N = 33Plots are as in Fig.11.1 and 11.2

Inference:Notice the maximum stopband attenuation of 53 dB from plot 11.2

%Design and implementation of FIR filter Method 1

%generate filter coefficients for the given %order & cutoff Say N=33, fc=150Hz, %fs=1000 Hz, Hamming windowh=fir1(33, 150/(1000/2),hamming(34));%generate simulated input of 50, 300 & 200 Hz, each of 30 pointsn=1:30;f1=50;f2=300;f3=200;fs=1000;x=[];x1=sin(2*pi*n*f1/fs);x2=sin(2*pi*n*f2/fs);x3=sin(2*pi*n*f3/fs);x=[x1 x2 x3];subplot(2,1,1);stem(x);title('input');%generate o/p%y=conv(h,x);y=filter(h,1,x);subplot(2,1,2);stem(y);title('output');

Result:Plots are in Fig.11.3 & 11.4Notice that freqs below 150 Hz are passed & above 150 are cutoff

Fig.11.1 Fig.11.2

Fig.11.3(using filter command) Fig.11.4 (using conv command)

Fig.11.5 Freqs f1=150;f2=300;f3=170;fs=1000;

FIR filter design using Kaiser window:

disp('FIR filter design using Kaiser window');M = input ('enter the length of the filter = ');beta= input ('enter the value of beta = ');Wc = input ('enter the digital cutoff frequency');Wn= kaiser(M,beta);disp('FIR Kaiser window coefficients');disp(Wn);hn = fir1(M-1,Wc,Wn);disp('the unit sample response of FIR filter is hn=');disp(hn);freqz(hn,1,512);grid on;xlabel('normalized frequency');ylabel('gain in db');title('freq response of FIR filter');

Result:FIR filter design using Kaiser windowenter the length of the filter = 30enter the value of beta = 1.2enter the digital cutoff frequency.3FIR Kaiser window coefficients 0.7175 0.7523 0.7854 0.8166 0.8457 0.8727 0.8973 0.9195 0.9392 0.9563 0.9706 0.9822 0.9909 0.9967 0.9996 0.9996 0.9967 0.9909 0.9822 0.9706 0.9563 0.9392 0.9195 0.8973 0.8727 0.8457 0.8166 0.7854 0.7523 0.7175

the unit sample response of FIR filter is hn= Columns 1 through 5 0.0141 0.0028 -0.0142 -0.0224 -0.0117 Columns 6 through 10 0.0133 0.0333 0.0277 -0.0072 -0.0495 Columns 11 through 15 -0.0614 -0.0140 0.0895 0.2097 0.2900 Columns 16 through 20 0.2900 0.2097 0.0895 -0.0140 -0.0614 Columns 21 through 25 -0.0495 -0.0072 0.0277 0.0333 0.0133 Columns 26 through 30 -0.0117 -0.0224 -0.0142 0.0028 0.0141

Fig.11.6.Response of FIR filter designed using Kaiser window

EXPERIMENT NO 8,9: Design and implementation of IIR filter to meet given specifications

Aim: To design and implement an IIR filter for given specifications.

Theory:There are two methods of stating the specifications as illustrated in previous program. In the first program, the given specifications are directly converted to digital form and the designed filter is also implemented. In the last two programs the butterworth and chebyshev filters are designed using bilinear transformation (for theory verification).Method I: Given the order N, cutoff frequency fc, sampling frequency fs and the IIR filter type (butterworth, cheby1, cheby2). Step 1: Compute the digital cut-off frequency Wc (in the range - < Wc < , with corresponding to fs/2) for fc and fs in Hz. For example let fc=400Hz, fs=8000HzWc = 2** fc / fs = 2* * 400/8000 = 0.1* radiansFor MATLAB the Normalized cut-off frequency is in the range 0 and 1, where 1 corresponds to fs/2 (i.e.,fmax)). Hence to use the MATLAB commandswc = fc / (fs/2) = 400/(8000/2) = 0.1Note: if the cut off frequency is in radians then the normalized frequency is computed as wc = Wc / Step 2: Compute the Impulse Response [b,a] coefficients of the required IIR filter and the response type (lowpass, bandpass, etc) using the appropriate butter, cheby1, cheby2 command. For example given a butterworth filter, order N=2, and a high pass response, the coefficients [b,a] of the filter are computed using the MATLAB inbuilt command butter as [b,a] =butter(N, wc , 'high');Method 2: Given the pass band (Wp in radians) and Stop band edge (Ws in radians) frequencies, Pass band ripple Rp and stopband attenuation As. Step 1: Since the frequencies are in radians divide by to obtain normalized frequencies to get wp=Wp/pi and ws=Ws/pi If the frequencies are in Hz (note: in this case the sampling frequency should be given), then obtain normalized frequencies as wp=fp/(fs/2), ws=fstop/(fs/2), where fp, fstop and fs are the passband, stop band and sampling frequencies in Hz Step 2: Compute the order and cut off frequency as [N, wc] = BUTTORD(wp, ws, Rp, Rs) Step 3: Compute the Impulse Response [b,a] coefficients of the required IIR filter and the response type as [b,a] =butter(N, wc , 'high');

IMPLEMENTATION OF THE IIR FILTER1. Once the coefficients of the IIR filter [b,a] are obtained, the next step is to simulate an input sequence x[n], say input of 100, 200 & 400 Hz (with sampling frequency of fs), each of 20/30 points. Choose the frequencies such that they are >, < and = to fc.2. Filter the input sequence x[n] with Impulse Response, to obtain the output of the filter y[n] using the filter command.3. Infer the working of the filter (low pass/ high pass, etc).

MATLAB IMPLEMENTATIONBUTTORD Butterworth filter order selection. [N, Wn] = BUTTORD(Wp, Ws, Rp, Rs) returns the order N of the lowest order digital Butterworth filter that loses no more than Rp dB in the passband and has at least Rs dB ofattenuation in the stopband. Wp and Ws are the passband and stopband edge frequencies, normalized from 0 to 1 (where 1 corresponds to pi radians/sample). For example, Lowpass: Wp = .1, Ws = .2 Highpass: Wp = .2, Ws = .1 Bandpass: Wp = [.2 .7], Ws = [.1 .8] Bandstop: Wp = [.1 .8], Ws = [.2 .7] BUTTORD also returns Wn, the Butterworth natural frequency (or, the "3 dB frequency") to use with BUTTER to achieve the specifications. [N, Wn] = BUTTORD(Wp, Ws, Rp, Rs, 's') does the computation for an analog filter, in which case Wp and Ws are in radians/second. When Rp is chosen as 3 dB, the Wn in BUTTER is equal to Wp in BUTTORD. BUTTER Butterworth digital and analog filter design. [B,A] = BUTTER(N,Wn) designs an Nth order lowpass digital Butterworth filter and returns the filter coefficients in length N+1 vectors B (numerator) and A (denominator). The coefficients are listed in descending powers of z. The cutoff frequency Wn must be 0.0 < Wn < 1.0, with 1.0 corresponding to half the sample rate. If Wn is a two-element vector, Wn = [W1 W2], BUTTER returns an order 2N bandpass filter with passband W1 < W < W2. [B,A] = BUTTER(N,Wn,'high') designs a highpass filter. [B,A] = BUTTER(N,Wn,'stop') is a bandstop filter if Wn = [W1 W2]. BUTTER(N,Wn,'s'), BUTTER(N,Wn,'high','s') and BUTTER(N,Wn,'stop','s') design analog Butterworth filters. In this case, Wn is in [rad/s] and it can be greater than 1.0.

Program (Design & implementation)%generate filter coefficients for the given %order & cutoff %Say N=2, fc=150Hz, %fs=1000 Hz, butterworth filter [b,a]=butter(2, 150/(1000/2));%generate simulated input of 100, 300 & 170 Hz, each of 30 pointsn=1:30;f1=100;f2=300;f3=170;fs=1000;x=[];x1=sin(2*pi*n*f1/fs);x2=sin(2*pi*n*f2/fs);x3=sin(2*pi*n*f3/fs);x=[x1 x2 x3];subplot(2,1,1);stem(x);title('input');%generate o/py=filter(b,a,x);subplot(2,1,2);stem(y);title('output');Result:Plot is in Fig. 12.1, which shows that 100 Hz is passed, while 300 is cutoff and 170 has slight attenuation.

Note: If fp,fstp,fs, rp,As are given then use[N,wc]=buttord(2*fp/fs,2*fstp/fs,rp,As)[b,a]=butter(N,wc);If wp & ws are in radians[N,wc]=buttord(wp/pi,ws/pi,rp,As)[b,a]=butter(N,wc);If wc is in radians & N is given[b,a]=butter(N,wc/pi);For a bandpass outputwc=[150/(1000/2) 250/(1000/2)];[b,a]=butter(4, wc);Plot is in Fig.12.2 where only 170 is passed, while 100 & 300 are cutoff.

Fig.12.1 Low pass IIR filter(butterworth) output

Fig.12.2 IIR output for bandpass (150-250 Hz)Programs for designing of IIR filters (for theory practice):% Butterworth filter: Given data: rp=1, rs=40, w1=800, %w2=1200,ws=3600;rp=1, rs=40, w1=800, w2=1200,ws=3600;

% Analog frequencyaw1=2*pi*w1/ws;aw2=2*pi*w2/ws;

% Prewrapped frequencypw1 = 2*tan(aw1/2);pw2 = 2*tan(aw2/2);

%Calculate order and cutoff freq[n,wc]= buttord (pw1,pw2,rp,rs,'s'); % analog filter transfer[b,a] = butter(n,wc,'s');

%obtaining the digital filter using bilinear transformationfs=1;[num,den]= bilinear(b,a,fs); %plot the frequency response[mag,freq1]=freqz(num,den,128);freq=freq1*ws/(2*pi);m = 20*log10(abs(mag));plot(freq,m);grid;

Result: rp = 1 rs = 40 w1 = 800 w2 = 1200

Fig. 12.2 BUTTERWORTH FILTER

To design a chebyshev filter for given specifications%Given datarp=1,rs=40,w1=800,w2=1200,ws=3600 %Analog frequenciesaw1= 2*pi*w1/ws;aw2=2*pi*w2/ws; % Prewrapped frequency assuming T=1/fspw1 = 2*tan(aw1/2);pw2 = 2*tan(aw2/2); [n,wc]= cheb1ord (pw1,pw2,rp,rs,'s'); [b,a] = cheby1(n,rp,wc,'s'); %obtaining the digital filter using bilinear transformationfs=1;[num,den]= bilinear(b,a,fs); %plot the frequency response[mag,freq1]=freqz(num,den,128);freq=freq1*ws/(2*pi);m = 20*log10(abs(mag));plot(freq,m);grid;

Result: rp = 1 rs = 40 w1 = 800 w2 = 1200 ws = 360

Fig.12.3 CHEBYSHEV FILTER

EXPERIMENT NO 10: Generation of sinusoidal wave using recursive difference equation

Aim: To generate sinusoidal signal using recursive difference equation

Theory:There exists different techniques to generate a sine wave on a DSP processor.Using a lookup table, interpolation, polynomials, or pulse width modulation are some of these techniques utilized to generate a sine wave. When a slightly sufficient processing power is considered, it is possible to utilize another technique which makes use of an IIR filter.In this technique, a second order IIR filter as shown in Figure 1 is designed to be marginally stable which is an undesirable situation for a normal filtering operation. For this second order filter to be marginally stable, its poles are located in the unit circle. After choosing the suitable filter coefficients for the filter to be marginally stable, a unit impulse is applied to the filter as an input at time t = 0, and then the input is disconnected. Then the filter starts to oscillate with a fixed frequency. The difference equation of the second order filter shown in Figure 1 will be:

Where F0 is the frequency, Fs is the sampling frequency and A is the amplitude of the sine wave.

Matlab Program

% Generation of sine wave using recursive difference equation or IIR filter%fs = sampling rate% t = time in sec%f0 = frequency% A = amplitudeA = 2;t=0.1;fs = 5000;f0=50;length = fs*t;y = zeros(1,length);impulse = zeros(1,length);impulse(1) = 1;% frequency coefficients% difference equation of form y[n]= -a1 y[n-1] -a2 y[n-2] + b0 impulse[n]% a1 = -2cos(2*pi*f0/fs), a2= 1, b0 = Asin(2*pi*f0/fs)a1 = -2*cos(2*pi*f0/fs)a2 = 1;b0 = A*sin(2*pi*f0/fs)y(1)= 0;y(2)= b0;for i=3:lengthy(i)= b0* impulse(i)- a1*y(i-1) - a2* y(i-2);endplot (y);title('Sinusoidal waveform')xlabel('samples n');ylabel('y(n)');

Result :

Experiment No. 11 Generation of DTMF signals

Theory: Dual-tone Multi-Frequency (DTMF) signaling is the basis for voice communications control and is widely used worldwide in modern telephony to dial numbers and configure switchboards. It is also used in systems such as in voice mail, electronic mail and telephone banking.

A DTMF signal consists of the sum of two sinusoids - or tones - with frequencies taken from two mutually exclusive groups. These frequencies were chosen to prevent any harmonics from being incorrectly detected by the receiver as some other DTMF frequency. Each pair of tones contains one frequency of the low group (697 Hz, 770 Hz, 852 Hz, 941 Hz) and one frequency of the high group (1209 Hz, 1336 Hz, 1477Hz) and represents a unique symbol. The frequencies allocated to the push-buttons of the telephone pad are shown below:1209Hz1336Hz1477 Hz

697Hz1ABC2DEF3

770 HzGHI4JKL5MNO6

852HzPQRS7TUV8WXYZ9

941 Hz*0#

We will right the program to generate and visualize the DTMF tones for all 12 Symbols. Also we will estimate its energy content using Gortzets Algorithm The minimum duration of a DTMF signal defined by the ITU standard is 40 ms. Therefore, there are at most 0.04 x 8000 = 320 samples available for estimation and detection. The DTMF decoder needs to estimate the frequencies contained in these short signals.

One common approach to this estimation problem is to compute the Discrete-Time Fourier Transform (DFT) samples close to the seven fundamental tones. For a DFT-based solution, it has been shown that using 205 samples in the frequency domain minimizes the error between the original frequencies and the points at which the DFT is estimated.

At this point we could use the Fast Fourier Transform (FFT) algorithm to calculate the DFT. However, the popularity of the Goertzel algorithm in this context lies in the small number of points at which the DFT is estimated. In this case, the Goertzel algorithm is more efficient than the FFT algorithm.

Plot Goertzel's DFT magnitude estimate of each tone on a grid corresponding to the telephone pad.

MATLAB Program

% generating all 12 frequencies symbol = {'1','2','3','4','5','6','7','8','9','*','0','#'};lfg = [697 770 852 941]; % Low frequency grouphfg = [1209 1336 1477]; % High frequency groupf = [];for c=1:4, for r=1:3, f = [ f [lfg(c);hfg(r)] ]; endendf'% Generate the DTMF tones Fs = 8000; % Sampling frequency 8 kHzN = 800; % Tones of 100 mst = (0:N-1)/Fs; % 800 samples at Fspit = 2*pi*t; tones = zeros(N,size(f,2));for toneChoice=1:12, % Generate tone tones(:,toneChoice) = sum(sin(f(:,toneChoice)*pit))'; % Plot tone subplot(4,3,toneChoice),plot(t*1e3,tones(:,toneChoice)); title(['Symbol "', symbol{toneChoice},'": [',num2str(f(1,toneChoice)),',',num2str(f(2,toneChoice)),']']) set(gca, 'Xlim', [0 25]); ylabel('Amplitude'); if toneChoice>9, xlabel('Time (ms)'); endendset(gcf, 'Color', [1 1 1], 'Position', [1 1 1280 1024])annotation(gcf,'textbox', 'Position',[0.38 0.96 0.45 0.026],... 'EdgeColor',[1 1 1],... 'String', '\bf Time response of each tone of the telephone pad', ... 'FitHeightToText', 'on');% estimation of DTMF tone using Gortzel's AlgorithmNt = 205;original_f = [lfg(:);hfg(:)] % Original frequenciesk = round(original_f/Fs*Nt); % Indices of the DFTestim_f = round(k*Fs/Nt) % Frequencies at which the DFT is estimatedtones = tones(1:205,:);figure,for toneChoice=1:12, % Select tone tone=tones(:,toneChoice); % Estimate DFT using Goertzel ydft(:,toneChoice) = goertzel(tone,k+1); % Goertzel use 1-based indexing % Plot magnitude of the DFT subplot(4,3,toneChoice),stem(estim_f,abs(ydft(:,toneChoice))); title(['Symbol "', symbol{toneChoice},'": [',num2str(f(1,toneChoice)),',',num2str(f(2,toneChoice)),']']) set(gca, 'XTick', estim_f, 'XTickLabel', estim_f, 'Xlim', [650 1550]); ylabel('DFT Magnitude'); if toneChoice>9, xlabel('Frequency (Hz)'); endendset(gcf, 'Color', [1 1 1], 'Position', [1 1 1280 1024])annotation(gcf,'textbox', 'Position',[0.28 0.96 0.45 0.026],... 'EdgeColor',[1 1 1],... 'String', '\bf Estimation of the frequencies contained in each tone of the telephone pad using Goertzel', ... 'FitHeightToText','on');

Output:

Experiment No.12: Implementation of Interpolation process

AIM: program to verify the decimation of given sequence.SOFTWARE: MATLAB 8.2Signal Processing ToolboxPROGRAM:clc;clear all;close all;%~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~%input signal%~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~fm=20;fs=500;t=0.1;dt=1/fs;n=0:dt:t;m=sin(2*pi*fm*n);subplot(2,1,1);stem(m);xlabel('n-->');ylabel('amplitude');title('input signal');%from the original signal interpolationb=input('enter the length of interpolation factor b= ');i=interp(m,b);subplot(2,1,2);stem(i);xlabel('n');ylabel('amplitude');title('interpolation from the original signal');OUTPUT:enter the length of interpolation factor b= 3

Experiment No.13: Implementation of Decimation process

AIM: program to verify the decimation of given sequence.SOFTWARE: MATLAB 8.2 Signal Processing ToolboxPROGRAM:clc;clear all;close all;%~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~%input signal%~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~fm=20;fs=1000;t=0.1;dt=1/fs;n=0:dt:t;m=sin(2*pi*fm*n);subplot(2,1,1);stem(m);xlabel('n-->');ylabel('amplitude');title('input signal');%~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~%decimation%~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~r=input(' enter the length of decimation factor r= ');d=decimate(m,r);subplot (2,1,2);stem(d);xlabel('n-->');ylabel('amplitude');title('decimation');OUTPUT:enter the length of decimation factor b= 3

Experiment No.14: Implementation of I/D sampling rate converters

AIM: program to implement sampling rate conversion.SOFTWARE: MATLAB 8.2Signal Processing ToolboxPROGRAM: clc;% clear comand window clear all; % clear work space close all; %close all figure windows %input signal N=50 ;% no of samples n=0:1:N-1; x=sin(2*pi*n/15);% input signal subplot(3,1,1) stem(n,x); xlabel('n'); ylabel('Amplitude'); title('Original Sequence'); %up sampling L=2;% upsampling factor x1=[zeros(1,L*N)]; n1=1:1:L*N; j =1:L:L*N; x1(j)=x; subplot(3,1,2) stem(n1-1,x1); xlabel('n'); ylabel('Amplitude'); title('Upsampled Sequence'); % down sampling M=2; x2=x(1:M:N); n2=1:1:N/M; subplot(3,1,3) stem(n2-1,x2); xlabel('n'); ylabel('Amplitude'); title('Downsampled Sequence');Output waveforms for Sampling Rate Conversion:

PART B - LIST OF EXPERIMENTS USING DSP PROCESSORARCHITECTURE AND INSTRUCTION SET OFDSPCHIP-TMS320C5515

Introduction to the TMS320C55x:The TMS320C55x digital signal processor (DSP) represents the latest generation of C5000 DSPs from Texas Instruments. The C55x is built on the proven legacy of the C54x and is source code compatible with the C54x, protecting the customers software investment. Following the trends set by the C54x, the C55x is optimized for power efficiency, low system cost, and best-in-class performance for tight power budgets. With core power dissipation as low as 0.05 mW/MIPS at 0.9V, and performance up to 800 MIPS (400 MHz), the TMS320C55x offers a cost-effective solution to the toughest challenges in personal and portable processing applications as well as digital communications infrastructure with restrictive power budgets. Compared to a 120-MHz C54x, a 300-MHz C55x will deliver approximately 5X higher performance and dissipate one-sixth the core power dissipation of the C54x. The C55x cores ultra-low power dissipation of 0.05mW/MIPS is achieved through intense attention to low-power design and advanced power management techniques. The C55x designers have implemented an unparalleled level of power-down configurability and granularity coupled with unprecedented power management that occurs automatically and is transparent to the user.The C55x core delivers twice the cycle efficiency of the C54x through a dual-MAC (multiply-accumulate) architecture with parallel instructions, additional accumulators, ALUs, and data registers. An advanced instruction set, a superset to that of the C54x, combined with expanded busing structure complements the new hardware execution units. The C55x continues the standard set by the C54x in code density leadership for lower system cost. The C55x instructions are variable byte lengths ranging in size from 8 bits to 48 bits. With this scalable instruction word length, the C55x can reduce control code size per function by up to 40% more than C54x. Reduced control code size means reduced memory requirements and lower system cost.

Key Features of the C55xThe C55x incorporates a rich set of features that provide processing efficiency, low-power dissipation, and ease of use. Some of these features are listed in Table

Overview of the C5515 eZdsp USB StickThe C5515 eZdsp USB Stick is an evaluation tool for the Texas Instruments TMS320C5515 Digital Signal Processor (DSP). This USB bus powered tool allows the user to evaluate the following items: The TMS320C5515 processor along with its peripherals The TLV320AIC3204 codec The Code Composer Studio IDETM software development tools

Key Features of the C5515 eZdsp USB StickThe C5515 eZdsp USB Stick has the following features: Texas Instruments TMS320C5515 Digital Signal Processor Texas Instruments TLV320AIC3204 Stereo Codec (stereo in, stereo out) Micro SD connector USB 2.0 interface to C5515 processor 32 Mb NOR flash I2C OLED display 5 user controlled LEDs 2 user readable push button switches Embedded USB XDS100 JTAG emulator Bluetooth board interface Expansion edge connector Power provided by USB interface Compatible with Texas Instruments Code Composer Studio v4 USB extension cableC5515 eZdsp USB Stick Block DiagramThe block diagram of the C5515 eZdsp USB Stick is shown below.

CODE COMPOSER STUDIO

INTRODUCTION TO CODE COMPOSER STUDIO

Code Composer Studio (CCS or CCStudio) is the integrated development environment for TI's DSPs, microcontrollers and application processors. CCStudio includes a suite of tools used to develop and debug embedded applications. It includes compilers for each of TI's device families, source code editor, project build environment, debugger, profiler, simulators and many other features. CCStudio provides a single user interface taking users through each step of the application development flow. Familiar tools and interfaces allow users to get started faster than ever before and add functionality to their application thanks to sophisticated productivity tools.CCStudio version 4 (CCSv4) is based on the Eclipse open source software framework. CCSv4 is based on Eclipse because it offers an excellent software framework for development environments a standard framework many embedded software vendors. CCSv4 combines the advantages of the Eclipse software framework with advanced embedded debug capabilities from TI resulting in a compelling feature rich development environment for embedded developers.

FeaturesDebuggerCCStudio's integrated debugger has several capabilities and advanced breakpoints to simplify development. Conditional or hardware breakpoints are based on full C expressions, local variables or registers. The advanced memory window allows you to inspect each level of memory so that you can debug complex cache coherency issues. CCStudio supports the development of complex systems with multiple processors or cores. Global breakpoints and synchronous operations provide control over multiple processors and cores.ProfilingCCStudio's interactive profiler makes it easy to quickly measure code performance and ensure the efficient use of the target's resources during debug and development sessions. The profiler allows developers to easily profile all C/C++ functions in their application for instruction cycles or other events such as cache misses/hits, pipeline stalls and branches.Profile ranges can be used to concentrate efforts on high-usage areas of code during optimization, helping developers produce finely-tuned code. Profiling is available for ranges of assembly, C++ or C code in any combination. To increase productivity, all profiling facilities are available throughout the development cycle.ScriptingSome tasks such as testing need to run for hours or days without user interaction. To accomplish such a task, the IDE should be able to automate common tasks. CCStudio has a complete scripting environment allowing for the automation of repetitive tasks such as testing and performance benchmarking. A separate scripting console allows you to type commands or to execute scripts within the IDE.Image Analysis and VisualizationCCStudio has many image analysis and graphic visualization. It includes the ability to graphically view variables and data on displays which can be automatically refreshed. CCStudio can also look at images and video data in the native format (YUV, RGB) both in the host PC or loaded in the target board.CompilerTI has developed C/C++ compilers specifically tuned to maximize the processor's usage and performance. TI compilers use a wide range of classical, application-oriented, and sophisticated device-specific optimizations that are tuned to all the supported architectures.Some of these optimizations include: Common sub-expression elimination Software pipelining Strength Reduction Auto increment addressing Cost-based register allocation Instruction predication Hardware looping Function In-lining VectorizationTI compilers also perform program level optimizations that evaluate code performance at the application level. With the program level view, the compiler is able to generate code similar to an assembly program developer who has the full system view. This application level view is leveraged by the compiler to make trade-offs that significantly increase the processor performance.The TI ARM and Microcontroller C/C++ compilers are specifically tuned for code size and control code efficiency. They offer industry leading performance and compatibility.SimulationSimulators provide a way for users to begin development prior to having access to a development board. Simulators also have the benefit of providing enhanced visibility into application performance and behavior. Several simulator variants are available allowing users to trade off cycle accuracy, speed and peripheral simulation, with some simulators being ideally suited to algorithm benchmarking and others for more detailed system simulation. Hardware Debugging (Emulation)TI devices include advanced hardware debugging capabilities. These capabilities include: IEEE 1149.1 (JTAG) and Boundary Scan Non-intrusive access to registers and memory Real-time mode which provides for the debugging of code that interacts with interrupts that must not be disabled. Real-time mode allows you to suspend background code at break events while continuing to execute time-critical interrupt service routines. Multi-core operations such as synchronous run, step, and halt. This includes crosscore triggering, which provides the ability to have one core trigger other cores to halt. Advanced Event Triggering (AET) which is available on selected devices, allows a user to halt the CPU or trigger other events based on complex events or sequences such as invalid data or program memory accesses. It can non-intrusively measure performance and count system events (for example, cache events).CCStudio provides Processor Trace on selected devices to help customers find previously invisible complex real-time bugs. Trace can detect the really hard to find bugs race conditions between events, intermittent real-time glitches, crashes from stack overflows, runaway code and false interrupts without stopping the processor. Trace is a completely nonintrusive debug method that relies on a debug unit inside the processor so it does not interfere or change the applications real-time behavior. Trace can fine tune code performance and cache optimization of complex switch intensive multi-channel applications. Processor Trace supports the export of program, data, timing and selected processor and system events/interrupts. Processor Trace can be exported either to an XDS560 Trace external JTAG emulator, or on selected devices, to an on chip buffer Embedded Trace Buffer (ETB).Real time operating system supportCCSv4 comes with two versions of TI's real time operating system: DSP/BIOS 5.4x is a real-time operating system that provides pre-emptive multitasking services for DSP devices. Its services include ISR dispatching, software interrupts, semaphores, messages, device I/O, memory management, and power management. In addition, DSP/BIOS 5.x also includes debug instrumentation and tooling, including low-overhead print and statistics gathering. BIOS 6.x is an advanced, extensible real-time operating system that supports ARM926, ARM Cortex M3, C674x, C64x+, C672x, and 28x-based devices. It offers numerous kernel and debugging enhancements not available in DSP/BIOS 5.x, including faster, more flexible memory management, events, and priority-inheritance mutexes.Note: BIOS 6.x includes a DSP/BIOS 5.x compatibility layer to support easy migration of application source code.Step 1:Open the code composer studio (CCSV4) and give a name to workspace and store it in the default path itself.Note: dont assign other than default path unless you are familiar with eclipse frame work based CCSV4

Step 2:Project windows overview

TMS320C6713 DSK CODEC(TLV320AIC23) Configuration Using Board Support Library

1.0 Unit Objective:To configure the codec TLV320AIC23 for a talk through program using the board support library.

2.0 PrerequisitesTMS320C6713 DSP Starter Kit, PC with Code Composer Studio, CRO, Audio Source, Speakers and Signal Generator.

3.0 Discussion on Fundamentals:Refer BSL API Module under, help contents TMS320C6713 DSK.

4.0 Procedure All the Real time implementations covered in the Implementations module follow code Configuration using board support library. The board Support Library (CSL) is a collection of functions, macros, and symbols used to configure and control on-chip peripherals. The goal is peripheral ease of use, shortened development time, portability, hardware abstraction, and some level of standardization and compatibility among TI devices. BSL is a fully scalable component of DSP/BIOS. It does not require the use of other DSP/BIOS components to operate.

Source Code: codec.c

Steps: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.4. Now Switch on the DSK and Bring Up Code Composer Studio on the PC.5. Create a new project with name XXXX.pjt.6. From the File Menu new DSP/BIOS Configuration select dsk6713.cdb and save it as YYYY.cdb and add it to the current project. 7. Add the generated YYYYcfg.cmd file to the current project.8. Add the given codec.c file to the current project which has the main function and calls all the other necessary routines.9. View the contents of the generated file YYYYcfg_c.c and copy the include header file YYYYcfg.h to the codec.c file.10. Add the library file dsk6713bsl.lib from the location C:\ti\C5400\dsk6713\lib\dsk6713bsl.lib to the 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 codec configuration. 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.

5.0 Conclusion:

The codec TLV320AIC23 successfully configured using the board support library and verified.

codec.c

#include "filtercfg.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 */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);}

EXP.NO: 15COMPUTATION OF N- POINT DFT OF A GIVEN SEQUENCEAim: To compute the N (=4/8/16) point DFT of the given sequenceEQUIPMENTS: Host (PC) with windows (95/98/Me/XP/NT/2000). TMS320C5515 DSP Starter Kit (DSK).Theory:

The N point DFT of discrete time signal x[n] is given by the equation

Where N is chosen such that , where L=length of x[n]. To implement using C program we use the expression and allot memory space for real and imaginary parts of the DFT X(k)Program//dft.c N-point DFT of sequence read from lookup table#include #include #define PI 3.14159265358979#define N 64#define TESTFREQ 10000.0#define SAMPLING_FREQ 64000.0typedef struct{float real;float imag;} COMPLEX;float x1[N],y1[N];COMPLEX samples[N];void dft(COMPLEX *x){COMPLEX result[N];int k,n,i;for (k=0 ; k