initial commit / add all books

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diff --git a/830/CH12/EX12.1.1/spectrum_of_signal.sce b/830/CH12/EX12.1.1/spectrum_of_signal.sce
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+++ b/830/CH12/EX12.1.1/spectrum_of_signal.sce
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+//Graphical//

+//Example 12.1.1

+//Determination of spectrum of a signal

+//With maximum normalized frequency f = 0.1

+//using Rectangular window and Blackmann window

+clear;

+close;

+clc;

+N = 61;

+cfreq = [0.1 0];

+[wft,wfm,fr]=wfir('lp',N,cfreq,'re',0);

+wft;                // Time domain filter coefficients

+wfm;                // Frequency domain filter values

+fr;                 // Frequency sample points

+WFM_dB = 20*log10(wfm);//Frequency response in dB

+for n = 1:N

+ h_balckmann(n)=0.42-0.5*cos(2*%pi*n/(N-1))+0.08*cos(4*%pi*n/(N-1));

+end

+wft_blmn = wft'.*h_balckmann;

+wfm_blmn = frmag(wft_blmn,length(fr));

+WFM_blmn_dB =20*log10(wfm_blmn);

+subplot(2,1,1)

+plot2d(fr,WFM_dB)

+xtitle('Frequency Response of Rectangular window Filtered output M = 61','Frequency in cycles per samples  f','Energy density in dB')

+subplot(2,1,2)

+plot2d(fr,WFM_blmn_dB)

+xtitle('Frequency Response of Blackmann window Filtered output M = 61','Frequency in cycles per samples  f','Energy density in dB')

diff --git a/830/CH12/EX12.1.2/spectrum_using_DFT.sce b/830/CH12/EX12.1.2/spectrum_using_DFT.sce
new file mode 100755
index 000000000..506617bcb
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+++ b/830/CH12/EX12.1.2/spectrum_using_DFT.sce
@@ -0,0 +1,81 @@
+//Graphical//

+//Example 12.1.2

+//Evaluating power spectrum of a discrete sequence

+//Using N-point DFT

+clear;

+clc;

+close;

+N =16;  //Number of samples in given sequence

+n =0:N-1;

+delta_f = [0.06,0.01];//frequency separation

+x1 = sin(2*%pi*0.315*n)+cos(2*%pi*(0.315+delta_f(1))*n);

+x2 = sin(2*%pi*0.315*n)+cos(2*%pi*(0.315+delta_f(2))*n);

+L = [8,16,32,128];

+k1 = 0:L(1)-1;

+k2 = 0:L(2)-1;

+k3 = 0:L(3)-1;

+k4 = 0:L(4)-1;

+fk1 = k1./L(1);

+fk2 = k2./L(2);

+fk3 = k3./L(3);

+fk4 = k4./L(4);

+for i =1:length(fk1)

+  Pxx1_fk1(i) = 0;

+  Pxx2_fk1(i) = 0;

+  for m = 1:N

+    Pxx1_fk1(i)=Pxx1_fk1(i)+x1(m)*exp(-sqrt(-1)*2*%pi*(m-1)*fk1(i));

+    Pxx2_fk1(i)=Pxx1_fk1(i)+x1(m)*exp(-sqrt(-1)*2*%pi*(m-1)*fk1(i));

+  end

+  Pxx1_fk1(i) = (Pxx1_fk1(i)^2)/N;

+  Pxx2_fk1(i) = (Pxx2_fk1(i)^2)/N;

+end

+for i =1:length(fk2)

+  Pxx1_fk2(i) = 0;

+  Pxx2_fk2(i) = 0;

+  for m = 1:N

+    Pxx1_fk2(i)=Pxx1_fk2(i)+x1(m)*exp(-sqrt(-1)*2*%pi*(m-1)*fk2(i));

+    Pxx2_fk2(i)=Pxx1_fk2(i)+x1(m)*exp(-sqrt(-1)*2*%pi*(m-1)*fk2(i));

+  end

+  Pxx1_fk2(i) = (Pxx1_fk2(i)^2)/N;

+  Pxx2_fk2(i) = (Pxx1_fk2(i)^2)/N;

+end

+for i =1:length(fk3)

+  Pxx1_fk3(i) = 0;

+  Pxx2_fk3(i) = 0;

+  for m = 1:N

+    Pxx1_fk3(i) =Pxx1_fk3(i)+x1(m)*exp(-sqrt(-1)*2*%pi*(m-1)*fk3(i));

+    Pxx2_fk3(i) =Pxx1_fk3(i)+x1(m)*exp(-sqrt(-1)*2*%pi*(m-1)*fk3(i));

+  end

+  Pxx1_fk3(i) = (Pxx1_fk3(i)^2)/N;

+  Pxx2_fk3(i) = (Pxx1_fk3(i)^2)/N;

+end

+for i =1:length(fk4)

+  Pxx1_fk4(i) = 0;

+  Pxx2_fk4(i) = 0;

+  for m = 1:N

+    Pxx1_fk4(i) =Pxx1_fk4(i)+x1(m)*exp(-sqrt(-1)*2*%pi*(m-1)*fk4(i));

+    Pxx2_fk4(i) =Pxx1_fk4(i)+x1(m)*exp(-sqrt(-1)*2*%pi*(m-1)*fk4(i));

+  end

+  Pxx1_fk4(i) = (Pxx1_fk4(i)^2)/N;

+  Pxx2_fk4(i) = (Pxx1_fk4(i)^2)/N;

+end

+figure

+title('for frequency separation = 0.06')

+subplot(2,2,1)

+plot2d3('gnn',k1,abs(Pxx1_fk1))

+subplot(2,2,2)

+plot2d3('gnn',k2,abs(Pxx1_fk2))

+subplot(2,2,3)

+plot2d3('gnn',k3,abs(Pxx1_fk3))

+subplot(2,2,4)

+plot2d3('gnn',k4,abs(Pxx1_fk4))

+figure

+title('for frequency separation = 0.01')

+subplot(2,2,1)

+plot2d3('gnn',k1,abs(Pxx2_fk1))

+subplot(2,2,2)

+plot2d3('gnn',k2,abs(Pxx2_fk2))

+subplot(2,2,3)

+plot2d3('gnn',k3,abs(Pxx2_fk3))

+subplot(2,2,4)

+plot2d3('gnn',k4,abs(Pxx2_fk4))

diff --git a/830/CH12/EX12.5.1/Additive_Noise_Parameters.sce b/830/CH12/EX12.5.1/Additive_Noise_Parameters.sce
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index 000000000..b59e316b7
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+++ b/830/CH12/EX12.5.1/Additive_Noise_Parameters.sce
@@ -0,0 +1,26 @@
+//Graphical//

+//Example 12.5.1

+//Determination of power, frequency and varaince of

+//Additive noise

+clear;

+clc;

+close;

+ryy = [0,1,3,1,0];//Autocorrelation of signal

+cen_ter_value = ceil(length(ryy)/2);//center value of autocorrelation

+//Method1

+//TO find out the variance of the additive Noise

+C = ryy(ceil(length(ryy)/2):$);

+corr_matrix = toeplitz(C);//correlation matrix

+evals =spec(corr_matrix);//Eigen Values computation

+sigma_w = min(evals);//Minimum of eigen value = varinace of noise

+//Method2

+//TO find out the variance of the additive Noise

+P = [1,-sqrt(2),1];//Ploynomial in decreasing order

+Z = roots(P);//roots of the polynomial

+P1 = ryy(cen_ter_value+1)/real(Z(1));//power of the sinusoid

+A = sqrt(2*P1);//amplitude of the sinusoid

+sigma_w1 = ryy(cen_ter_value)-P1;//variance of noise method2

+disp(P1,'Power of the additive noise')

+f1 = acos(real(Z(1)))/(2*%pi)

+disp(f1,'frequency of the additive noise')

+disp(sigma_w1,'Variance of the additive noise')