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Published on November 17, 2016

Author: RakeshThakur39

Source: slideshare.net

1. JAWAHAR LAL NEHRU GOVERNMENT ENGINEERING COLLEGE SUNDERNAGAR (175018) PRACTICAL FILE OF DIGITAL SIGNAL PROCESSING EC- 413(P) SUBMITTED TO: SUBMITTED BY: ER. MUNISH BHARDWAJ RAKESH KUMAR THAKUR BT-30663 7TH SEMESTER

2. INDEX S.No. Experiment Remarks 1. Introduction to Matlab 4-7 2. Representation of basic signal 8-11 3. Representation of sinusoidal signals 12-15 4. To study Quantization technique 16-19 5. To study sampling theorem 20-23 6. To develop program for linear convulation 24-27 7. To develop program for autocorrelation 28-31 8. To develop program for cross-correlation 32-35 9. To study ASK,FSK and PSK 36-41 10. To study window Technique 42-45 11. To generate triangular and square wave 46-49

3. EXPERIMENT- 1 AIM: Introduction to MATLAB

4. EXPERIMENT- 1 AIM: Introduction to MATLAB THEORY: Introduction to MATLAB Matlab is an interpreted language for numerical computation. It allows one to perform numerical calculations, and visualize the results without the need for complicated and time consuming programming. Matlab allows its users to accurately solve problems, produce graphics easily and produce code efficiently. MATLAB programs are stored as plain text in files having names that end with the extension “m”. These files are called m-files. MATLAB functions have two parameter lists, one for input and one for output. One nifty difference between MATLAB and traditional high level languages is that MATLAB functions can be used interactively. In addition to providing the obvious support for interactive calculation, it also is a very convenient way to debug functions that are part of a bigger project. Windows:  Command Window: The window where we type commands and non-graphic output is displayed. A ‘>>’ prompt shows the system is ready for input. The lower left hand corner of the main window also displays ‘ready’ or ‘Busy’ when the system is waiting or calculating. Previous commands can be accessed using the up arrow to save typing and reduce errors. Typing a few characters restricts this function to commands beginning with those characters.  Figure Window: MATLAB directs graphics output to a window that is separate from the command window. In MATLAB, this window is referred to as a figure. Graphics functions automatically create new figure windows if none currently exist. If a figure window already exists, MATLAB uses that window. If multiple figure windows exist, one is designated as the current figure and is used by MATLAB.  Editor Window: The window where we edit m-files the files that hold scripts and functions that we’ve defined or are editing. Multiple files are generally opened as tabs in the same editor window, but they can also be tiled for side by side comparison. Orange warnings and red errors appear as underlining and as bars in the margin. Covering over them provides more information/clicking on the bar takes us to the

5. Relevant bit of text. Also MATLAB runs the last saved version of a file, so we have to save before any changes take effect. Commands: clc: Clears command window clear all: Deletes all variables from current workspace close all: Closes all open figure windows for (loop): Iterates over procedure incrementing i by 1 Subplot: Divide the plot window up into pieces Stem: Plot discrete sequence data Plot:  plot(x,y): Plot the elements of vector y (on the vertical axis of a figure) versus the elements of the vector x (on the horizontal axis of the figure).  plot(w,abs(y)): Plot the magnitude of vector y (on the vertical axis of a figure) versus the elements of the vector w (on the horizontal axis of the figure).  plot(w,angle(y)): Plot the phase of vector y in radian (on the vertical axis of a figure) versus the elements of the vector w (on the horizontal axis of the figure). Label:  X label: Add a label to the horizontal axis of the current plot.  Y label: Add a label to the vertical axis of the current plot. Title: Add a title to the current plot

6. EXPERIMENT- 2 AIM: Write a program in MATLAB to generate the following waveforms: Unit impulse signal, Unit step signal, Ramp signal, Exponential signal; APPARATUS REQUIRED: Computer, MATLAB software;

7. EXPERIMENT- 2 AIM: Write a program in MATLAB to generate the following waveforms: Unit impulse signal, Unit step signal, Ramp signal, Exponential signal; APPARATUS REQUIRED: Computer, MATLAB software; THEORY: Real signals can be quite complicated. The study of signals therefore starts with the analysis of basic and fundamental signals. For linear systems, a complicated signal and its behaviour can be studied by superposition of basic signals. Common basic signals are: Discrete – Time signals:  Unit impulse sequence.  Unit step sequence.  Unit ramp sequence.  Exponential sequence. x(n) = A an , where A and a are constant SOURCE CODE: %WAVE FORM GENERATION %UNIT IMPULSE clc; clear all; close all; n1 = -3:1:3; x1 = [0,0,0,1,0,0,0]; subplot(2,2,1); stem(n1,x1); xlabel('time'); ylabel('Amplitude'); x n n n ( ) ( ) ,       1 0 0 for , otherwise x n u n n ( ) ( ) ,      1 0 0 for , otherwise x n r n n n ( ) ( ) ,      for , otherwise 0 0

8. RESULT: The program to generate various waveforms is written, executed and the output is verified

9. title('Unit impulse signal'); %UNIT STEP SIGNAL n2=-5:1:25; x2=[zeros(1,5),ones(1,26)]; subplot(2,2,2); stem(n2,x2); xlabel('time'); ylabel('Amplitude'); title('Unit step signal'); %EXPONENTIAL SIGNAL a=5; n3=-10:1:20; x3=power(a,n3); subplot(2,2,3); stem(n3,x3); xlabel('time'); ylabel('Amplitude'); title('Exponential signal'); %UNIT RAMP SIGNAL n4=-10:1:20; x4=n4; subplot(2,2,4); stem(n4,x4); xlabel('time'); ylabel('Amplitude'); title('Unit ramp signal'); RESULT: The program to generate various waveforms is written, executed and the output is verified.

10. EXPERIMENT- 3 AIM: Write a program in MATLAB to generate the following waveforms: Sine and Cosine signal; APPARATUS REQUIRED: Computer, MATLAB software;

11. EXPERIMENT- 3 AIM: Write a program in MATLAB to generate the following waveforms: Sine and Cosine signal; APPARATUS REQUIRED: Computer, MATLAB software; THEORY: Real signals can be quite complicated. The study of signals therefore starts with the analysis of basic and fundamental signals. For linear systems, a complicated signal and its behaviour can be studied by superposition of basic signals. Common basic signals are: Sinusoidal signal. SOURCE CODE: %WAVE FORM GENERATION %SINE SIGNAL A=input('Enter the amplitude:'); f=input('Enter the frequency:'); n1=-pi:0.03:pi; x1=A*sin(2*f*n1); subplot(2,1,1); stem(n1,x1); xlabel('time'); ylabel('Amplitude'); title('Sine signal'); %COSINE SIGNAL x t A( ) sin( ) t  

12. RESULT: The program to generate various waveforms is written, executed and the output is verified.

13. A=input('Enter the amplitude:'); f=input('Enter the frequency:'); n2=-pi:0.03:pi; x2=A*cos(2*f*n2); subplot(2,1,2); stem(n2,x2); xlabel('time'); ylabel('Amplitude'); title('Cosine signal'); RESULT: The program to generate various waveforms is written, executed and the output is verified.

14. EXPERIMENT- 4 AIM: To develop program for quantization APPARATUS REQUIRED: PC, MATLAB software

15. EXPERIMENT- 4 AIM: To develop program for quantization APPARATUS REQUIRED: PC, MATLAB software THEORY: Quantization: A continuous time signal, such as voice, has a continuous range of amplitudes and therefore its samples have a continuous amplitude range i.e. they are only discrete in time not in amplitude. In other words, within the finite amplitude range of the signal, we find an infinite number of amplitude levels. It is not necessary in fact to transmit the exact amplitude of the samples. Any human sense (the ear or the eye), as ultimate receiver, can detect only finite intensity differences. This means that the original continuous time signal may be approximated by a signal constructed of discrete amplitudes selected on a minimum error basis from an available set. Clearly, if we assign the discrete amplitude levels with sufficiently close spacing we may take the approximated signal practically indistinguishable from the original continuous signal. Amplitude quantization is defined as the process of transforming the sample amplitude m(nTs) of a message signal m(t) at time t=nTs into a discrete amplitude v(nTs) taken from a finite set of possible amplitudes. SOURCE CODE: %MATLAB code for ask fsk and psk clc; clear all; close all; %input signal t=0:0.1:2*pi; y=sin(t); %quantizing input signal z=round(y); %ploting signals plot(y); hold all; stem(y,'g'); hold all; stem(z,'r'); hold all;

16. RESULT: The quantization is performed by using MATLAB script

17. xlabel('time'); ylabel('Amplitude'); title('Quantization'); RESULT: The quantization performed by using MATLAB script

18. EXPERIMENT- 5 AIM: To understand sampling theorem. APPARATUS REQUIRED: PC, MATLAB software

19. EXPERIMENT- 5 AIM: To understand sampling theorem. APPARATUS REQUIRED: PC, MATLAB software THEORY: SAMPLING PROCESS: It is a process by which a continuous time signal is converted into discrete time signal. X[n] is the discrete time signal obtained by taking samples of the analog signal x(t) every T seconds, where T is the sampling period. X[n] = x (t) x p (t) Where p(t) is impulse train; T – period of the train SAMPLING THEOREM: It states that the band limited signal x(t) having no frequency components above Fmax Hz is specified by the samples that are taken at a uniform rate greater than 2 Fmax Hz (Nyquist rate), or the frequency equal to twice the highest frequency of x(t). Fs ≥ 2 Fmax SOURCE CODE: clc; clear all; close all; %continuous sinusoidal signal a=input('Enter the amplitude :'); f=input('Enter the Timeperiod :'); t=-pi:0.3:pi;

20. RESULT: The sampling theorem performed by using MATLAB script

21. x=a*sin(2*f*t); subplot(4,1,1); plot(t,x); xlabel('time');ylabel('Amplitude'); title('Sinusoidal signal'); %sampling without distortion fs=input('enter sampling frequency(fs=>2*f) : '); y=a*sin(2*f*fs*t); subplot(4,1,2); plot(t,y); xlabel('time'); ylabel('Amplitude'); title('Distortion less Sinusoidal signal'); %critical sampling fs=input('enter sampling frequency fs=2*f : '); z=a*sin(2*f*fs*t); subplot(4,1,3); plot(t,z); xlabel('time'); ylabel('Amplitude'); title('Sampled at Nyquist rate Sinusoidal signal'); %sampling with distortion fs=input('enter sampling frequency fs=2*f : '); u=a*sin(2*f*fs*t); subplot(4,1,4); plot(t,u); xlabel('time'); ylabel('Amplitude'); title('Distorted Sinusoidal signal'); RESULT: The sampling theorem performed by using MATLAB script

22. EXPERIMENT- 6 AIM: Write a MATLAB Script to perform discrete convolution (Linear) for the given two sequences. APPARATUS REQUIRED: PC, MATLAB software

23. EXPERIMENT- 6 AIM: Write a MATLAB Script to perform discrete convolution (Linear) for the given two sequences. APPARATUS REQUIRED: PC, MATLAB software THEORY: LINEAR CONVOLUTION: The response y[n] of a LTI system for any arbitrary input x[n] is given by convolution of impulse response h[n] of the system and the arbitrary input x[n]. y[n] = x[n]*h[n] =     k knhkx ][][ or     k knxkh ][][ If the input x[n] has N1 samples and impulse response h[n] has N2 samples then the output sequence y[n] will be a finite duration sequence consisting of (N1 + N2 - 1) samples. SOURCE CODE: clc; clear all; close all; %Program to perform Linear Convolution x1=input('Enter the first sequence to be convoluted:'); subplot(3,1,1); stem(x1); xlabel('Time'); ylabel('Amplitude'); title('First sequence'); x2=input('Enter the second sequence to be convoluted:');

24. RESULT: The linear convolutions are performed by using MATLAB script;

25. subplot(3,1,2); stem(x2); xlabel('Time'); ylabel('Amplitude'); title('Second sequence'); f=conv(x1,x2); disp('The Linear convoluted sequence is'); disp(f); subplot(3,1,3); stem(f); xlabel('Time'); ylabel('Amplitude'); title('Linear Convoluted sequence'); RESULT: The linear convolutions are performed by using MATLAB script;

26. EXPERIMENT- 7 AIM: To develop program for autocorrelation APPARATUS REQUIRED: PC, MATLAB software

27. EXPERIMENT- 7 AIM: To develop program for autocorrelation APPARATUS REQUIRED: PC, MATLAB software THEORY: AUTOCORRELATION: Autocorrelation is the cross-correlation of a signal with itself. Informally, it is the similarity between observations as a function of the time lag between them. It is a mathematical tool for finding repeating patterns, such as the presence of a periodic signal obscured by noise, or identifying the missing fundamental frequency in a signal implied by its harmonic fre3uencies. It is often used in signal processing for analyzing functions or series of values, such as time domain signals. SOURCE CODE: clc; clear all; x = input(‘enter the finite length signal sequence’); N = 0:length(x)-1; %perform autocorrelation using corr function y = xcorr(x,x); %generating time index for the autocorrelation sequence N2 = -length(x)+1:length(x)-1; %plot original signal and autocorrelation sequence subplot(2,1,1); stem(N,x); xlabel('N'); ylabel('x(n)'); title('original signal'); subplot(2,1,1); stem(N2,y); xlabel('N'); ylabel('y(n)');

28. RESULT: The autocorrelation is performed by using MATLAB script;

29. title('Autocorrelation signal'); RESULT: The autocorrelation is performed by using MATLAB script;

30. EXPERIMENT- 8 AIM: To perform cross-correlation of a given sequence. APPARATUS REQUIRED: PC, MATLAB software

31. EXPERIMENT- 8 AIM: To perform cross-correlation of a given sequence. APPARATUS REQUIRED: PC, MATLAB software THEORY: CROSS-CORRELATION: In signal processing, cross-correlation is a measure of similarity of two waveforms as a function of a time lag applied to one of them. This is also known as a sliding dot product or sliding inner product. It is commonly used for searching a long signal for a shorter, known feature. It has applications in pattern recognition, single particle analysis, electron tomographic averaging; The cross-correlation is similar in nature to the convolution of two functions. In an autocorrelation, which is the cross-correlation of a signal with itself, there will always be a peak at a lag of zero unless the signal is a trivial zero signal. In probability theory and statistics, correlation is always used to include a standardizing factor in such a way that correlations have values between -1 and +1, and the term cross-correlation is used for referring to the correlation corr(X,Y), between two random variables X and Y, while the “correlation” of a random vector X is considered to be the correlation matrix (matrix of correlations) between the scalar elements of X. SOURCE CODE: clc; clear all; x1 = input('enter 1st the finite length signal sequence x1(n) : '); n1 = 0:length(x1)-1; x2 = input('enter 2nd the finite length signal sequence x2(n) : '); n2 = 0:length(x2)-1; %perform cross-correlation using corr function y12 = xcorr(x1,x2); y21 = xcorr(x2,x1); %generating time index for the cross correlation sequence N1 = -length(x1)+1:length(x1)-1; N2 = -length(x2)+1:length(x2)-1;

32. RESULT: The cross-correlation are performed by using MATLAB script;

33. %plot original signal and cross correlation sequence subplot(4,1,1); stem(n1,x1); xlabel('N'); ylabel('x1(n)'); title('original signal x1(n)'); subplot(4,1,2); stem(n2,x2); xlabel('N'); ylabel('x2(n)'); title('original signal x2(n)'); subplot(4,1,3); stem(y12); xlabel('N1'); ylabel('y12(n)'); title('Cross-correlated sequence'); subplot(4,1,4); stem(y21); xlabel('N2'); ylabel('y21(n)'); title('Cross-correlated sequence'); RESULT: The cross-correlation are performed by using MATLAB script;

34. EXPERIMENT- 9 AIM: To develop program for ASK, FSK and PSK on Matlab script. APPARATUS REQUIRED: PC, MATLAB software

35. EXPERIMENT- 9 AIM: To develop program for ASK, FSK and PSK on Matlab script. APPARATUS REQUIRED: PC, MATLAB software THEORY:  Amplitude shift keying ASK is a modulation process, which imparts to a sinusoid two or more discrete amplitude levels. These are related to the number of levels adopted by the digital message. For a binary message sequence there are two levels, one of which is typically zero. The data rate is a sub-multiple of the carrier frequency. Thus the modulated waveform consists of bursts of a sinusoid. One of the disadvantages of ASK, compared with FSK and PSK, is that it has not got a constant envelope. This makes its processing (e.g., power amplification) more difficult, since linearity becomes an important factor. However, it does make for ease of demodulation with an envelope detector.  Phase-shift keying (FSK) PSK is a digital modulation scheme that conveys data by changing, or modulating, the phase of a reference signal (the carrier wave). PSK uses a finite number of phases, each assigned a unique pattern of binary digits. Usually, each phase encodes an equal number of bits. Each pattern of bits forms the symbol that is represented by the particular phase. The demodulator, which is designed specifically for the symbol-set used by the modulator, determines the phase of the received signal and maps it back to the symbol it represents, thus recovering the original data. In a coherent binary PSK system, the pair of signal S1(t) and S2 (t) used to represent binary symbols 1 & 0 are defined by S1 (t) = √2E/ Tb Cos 2πfct S2 (t) =√2E/Tb (2πfct+π) = - √ 2Eb/Tb Cos 2πfct where 0 ≤ t< Tb and E = Transmitted signed energy for bit The carrier frequency fc =n/Tb for some fixed integer n.

36.  Frequency-shift keying (FSK) Frequency-shift keying (FSK) is a frequency modulation scheme in which digital information is transmitted through discrete frequency changes of a carrier wave. The simplest FSK is binary FSK (BFSK). BFSK uses a pair of discrete frequencies to transmit binary (0s and 1s) information. With this scheme, the "1" is called the mark frequency and the "0" is called the space frequency. In binary FSK system, symbol 1 & 0 are distinguished from each other by transmitting one of the two sinusoidal waves that differ in frequency by a fixed amount. Si (t) = √2E/Tb cos 2πfit 0≤ t ≤Tb 0 elsewhere Where i=1, 2 E=Transmitted energy/bit Transmitted freq= ƒi = (nc+i)/Tb, and n = constant (integer),Tb = bit interval Symbol 1 is represented by S1 (t) Symbol 0 is represented by S0 (t) SOURCE CODE: %MATLAB code for ask fsk and psk clc; clear all; f=5; f2=10; x=[1 1 0 0 1 0 1 0] ; % input signal n=length(x); i=1; while i<n+1 t = i:0.001:i+1; if x(i)==1 ask=sin(2*pi*f*t); fsk=sin(2*pi*f*t); psk=sin(2*pi*f*t); else ask=0; fsk=sin(2*pi*f2*t); psk=sin(2*pi*f*t+pi); end

37. RESULT: The ASK, FSK, PSK studied and are performed by using MATLAB script;

38. subplot(3,1,1); plot(t,ask); hold on; ylabel ('Amplitude'); xlabel ('Time'); title('Amplitude Shift Key'); subplot(3,1,2); plot(t,fsk); hold on; ylabel ('Amplitude'); xlabel ('Time'); title('Frequency Shift Key') subplot(3,1,3); plot(t,psk); hold on; ylabel ('Amplitude'); xlabel ('Time'); title('Phase Shift Key') i=i+1; end RESULT: The ASK, FSK, PSK studied and are performed by using MATLAB script;

39. EXPERIMENT- 10 AIM: To study different window techniques APPARATUS REQUIRED: PC, MATLAB software

40. EXPERIMENT- 10 AIM: To study different window techniques APPARATUS REQUIRED: PC, MATLAB software THEORY: Most digital signals are infinite, or sufficiently large that the dataset cannot be manipulated as a whole. Sufficiently large signals are also difficult to analyze statistically, because statistical calculations require all points to be available for analysis. In order to avoid these problems, engineers typically analyze small subsets of the total data, through a process called windowing Consider the system H(z), with input X(z) and output Y(z). We model this as: If we have a window with transfer function W(z), we can mathematically apply the window to our signal, X(z) as such: Then, we can pass our windowed signal into our system, H(z) as usual: w = rectwin(L) returns a rectangular window of length L in the column vector w. This function is provided for completeness; a rectangular window is equivalent to no window at all. w = hamming(L) returns an L-point symmetric Hamming window in the column vector w. L should be a positive integer. The coefficients of a Hamming window are computed from the following equation. w(n)=0.54−0.46cos(2πnN), 0≤n≤N The window length is L=N+1 w = bartlett(L) returns an L-point Bartlett window in the column vector w, where L must be a positive integer. The coefficients of a Bartlett window are computed as follows:

41. RESULT: The window techniques studied and are generated by using MATLAB script

42. w(n)= 2n/N 2−2nN 0≤n≤N2 N2≤n≤N The window length L=N+1 w = blackman(N) returns the N-point symmetric Blackman window in the column vector w, where N is a positive integer. The following equation defines the Blackman window of length N: w(n)=0.42−0.5cos2πnN−1+0.08cos4πnN−1, 0≤n≤M−1 where M is N/2 for N even and (N + 1)/2 for N odd. SOURCE CODE: %Program to generate window %rectangular window L=10; w1=rectwin(L); wvtool(w); %% %bartlett window L=64; w2=bartlett(L); wvtool(w2); %% %hamming window L=64; w3=hamming(L); wvtool(w3); %% %blackman window L=64; w4=blackman(L); wvtool(w4); RESULT: The window techniques studied and are generated by using MATLAB script

43. EXPERIMENT- 11 AIM: To generate a triangular and square waveform. APPARATUS REQUIRED: PC, MATLAB software

44. EXPERIMENT- 11 AIM: To generate a triangular and square waveform. APPARATUS REQUIRED: PC, MATLAB software THEORY: SQUARE WAVE: Square wave is a non-sinusoidal periodic waveform (which can be represented as an infinite summation of sinusoidal waves), in which the amplitude alternates at a steady frequency between fixed minimum and maximum values, with the same duration at minimum and maximum. The transition between minimum to maximum is instantaneous for an ideal square wave; this is not realizable in physical systems. Square waves are often encountered in electronics and signal processing SOURCE CODE: %WAVE FORM GENERATION %PROGRAM TO GENERATE TRIANGULAR WAVE clc; clear all; n=input ('Enter the length of the sequence : '); t=0:.0001:n; y=sawtooth(t,.5); %sawtooth with 50% duty cycle (triangular) subplot(2,1,1); plot(t,y); ylabel ('Amplitude'); xlabel ('Time'); title ('Triangular waveform'); %PROGRAM TO GENERATE SQUARE WAVE n=input ('Enter the length of the sequence : '); t=0:.0001:n; y=square(t); subplot(2,1,2); plot(t,y); ylabel ('Amplitude'); xlabel ('Time');title ('Triangular waveform');

45. RESULT: The program to generate various waveforms is written, executed and the output is verified.

46. RESULT: The program to generate various waveforms is written, executed and the output is verified

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