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Adaptive Signal Processing Assignment 1 Note: There will be 50% penalty for late submission on same day. The subject field of your email must be Assignment1-RollNo For the programming portion, attach the zipped code files with your name. For example, assign01-StudentName-RegNo.zip. PART-I Q. No.1 I. Generate 1000 samples of zero mean unit variance normal (Gaussian) data using randn(). Assign to variable x. II. Transform random data generated in part I to get mean=2 and variance=0.5. Assign to variable x1. III. Transform random data generated in part I to get mean=10 and variance=5. Assign to variable x2. Use the following code to generate and plot histograms. Write your observations in separate MS Word File. [val1,nbar1]=hist(x,100); % Generate histogram using 100 bins [val2,nbar2]=hist(x1,100); [val3,nbar3]=hist(x2,100); figure(1) subplot(3,1,1); bar(nbar1,val1); % plot histogram against bins axis([-3 18 0 30]) % To plot data on same axis subplot(3,1,2); bar(nbar2,val2); axis([-3 18 0 30]) subplot(3,1,3); bar(nbar3,val3); axis([-3 18 0 30]) Q. No.2

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Page 1: · Web viewThere will be 50% penalty for late submission on same day. ... Write your observations in separate MS Word File. [val1,nbar1]=hist(x,100); % Generate histogram using

Adaptive Signal ProcessingAssignment 1

Note: There will be 50% penalty for late submission on same day. The subject field of your email must be Assignment1-RollNo For the programming portion, attach the zipped code files with your name.

For example, assign01-StudentName-RegNo.zip.

PART-I

Q. No.1I. Generate 1000 samples of zero mean unit

variance normal (Gaussian) data using randn(). Assign to variable x.

II. Transform random data generated in part I to get mean=2 and variance=0.5. Assign to variable x1.

III. Transform random data generated in part I to get mean=10 and variance=5. Assign to variable x2.

Use the following code to generate and plot histograms. Write your observations in separate MS Word File.[val1,nbar1]=hist(x,100); % Generate histogram using 100 bins[val2,nbar2]=hist(x1,100);[val3,nbar3]=hist(x2,100); figure(1)subplot(3,1,1); bar(nbar1,val1); % plot histogram against binsaxis([-3 18 0 30]) % To plot data on same axis subplot(3,1,2); bar(nbar2,val2);axis([-3 18 0 30])subplot(3,1,3); bar(nbar3,val3);axis([-3 18 0 30])

Q. No.2I. Generate 100 samples of standard

normal using randn(). Compute autocorrelation function using xcorr(). Plot using stem() from -50 lag to 50 lag as shown in figure.

Page 2: · Web viewThere will be 50% penalty for late submission on same day. ... Write your observations in separate MS Word File. [val1,nbar1]=hist(x,100); % Generate histogram using

II. Repeat same procedure for 1000 samples.III. Repeat same procedure for 10000 samples.

USE figure(2), subplot() and stem() to generate figure.Append your observations in MS Word File.

Q. No.3I. Generate a 0.01 second sinusoid s with

Frequency = 1e3;Amplitude =1;Sampling frequency =10e3;Add White Gaussian Noise with SNR=0 using sn=awgn(s,SNR,'measured'). Plot this noise corrupted signal. Compute autocorrelation and plot stems from -100 lags to 100 lags.

figure(4)subplot(3,2,1); stem(t,sn);subplot(3,2,3); stem(t,sn1);subplot(3,2,5); stem(t,sn2); subplot(3,2,2); stem(lag,y);subplot(3,2,4); stem(lag1,y1);subplot(3,2,6); stem(lag2,y2);

II. Repeat the procedure for SNR=10.III. Repeat the procedure for SNR=-10.

Append you observations in MS Word File.

Deliverables zipped in a single file named assign01-student-name-Reg.No.zip

1. Single m-file for the Qno.1 to Qno.3.2. Plots for the Qno.1 to Qno.3.3. MS-word file provide your observations.

PART-II

Solve the following problems given at the end of the Chapter 2 and 3.I. P2.1, P2.6, P2.11, P2.12.

II. P3.2, P3.3, P3.4, P3.9