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|
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Chapter 3 Detection & Estimation"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Example3.1 page 120"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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SZmYpSpqTGJVOly9dAeyU398JWBMRDUt8u5Mws7Gq3zqJlKzAy4FXSVoOLAM+2qxRdxJm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\nOO4C4GayK5VCed8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88T3AMRGxsqotdxxmZiMw2tVNZeZJbEqkA5aTJdLNrjpmLln1umvz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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7ff404cc5710>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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YDFyQXjjxeJJwzqWtVZJE2mMSrxIGpHNGEa4mNiNpL+AGYIKZvV2soUbOk/Ak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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7ff3eae3fad0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from __future__ import division\n",
"from numpy import arange, ones, sqrt\n",
"%matplotlib inline\n",
"from matplotlib.pyplot import plot, subplot, xlabel,ylabel,title, show\n",
"\n",
"#using Gram-Schmidt orthogonalization procedure\n",
"T = 1#\n",
"t1 = arange(0,0.01+T/3,0.01)\n",
"t2 = arange(0,0.01+2*T/3,0.01)\n",
"t3 = arange(T/3,0.01+T,0.01)\n",
"t4 = arange(0,0.01+T,0.01)\n",
"s1t = [0]+[x for x in ones(len(t1)-2)]+[0]\n",
"s2t = [0]+[x for x in ones(len(t2)-2)]+[0]\n",
"s3t = [0]+[x for x in ones(len(t3)-2)]+[0]\n",
"s4t = [0]+[x for x in ones(len(t4)-2)]+[0]\n",
"t5 = arange(0,0.01+T/3,0.01)\n",
"phi1t = [sqrt(3/T)*x for x in [0]+[x for x in ones(len(t5)-2)]+[0]]\n",
"t6 =arange(T/3,0.01+2*T/3,0.01)\n",
"phi2t = [sqrt(3/T)*x for x in [0]+[x for x in ones(len(t6)-2)]+[0]]\n",
"t7 = arange(2*T/3,0.01+T,0.01)\n",
"phi3t = [sqrt(3/T)*x for x in [0]+[x for x in ones(len(t7)-2)]+[0]]\n",
"\n",
"#figure\n",
"title('Figure3.4(a) Set of signals to be orthonormalized')\n",
"subplot(4,1,1)\n",
"plot(t1,s1t)\n",
"xlabel('t')\n",
"ylabel('s1(t)')\n",
"subplot(4,1,2)\n",
"plot(t2,s2t)\n",
"xlabel('t')\n",
"ylabel('s2(t)')\n",
"subplot(4,1,3)\n",
"plot(t3,s3t)\n",
"xlabel('t')\n",
"ylabel('s3(t)')\n",
"subplot(4,1,4)\n",
"plot(t4,s4t)\n",
"xlabel('t')\n",
"ylabel('s4(t)')\n",
"show()\n",
"\n",
"\n",
"#figure\n",
"title('Figure3.4(b) The resulting set of orthonormal functions')\n",
"subplot(3,1,1)\n",
"plot(t5,phi1t)\n",
"xlabel('t')\n",
"ylabel('phi1(t)')\n",
"subplot(3,1,2)\n",
"plot(t6,phi2t)\n",
"xlabel('t')\n",
"ylabel('phi2(t)')\n",
"subplot(3,1,3)\n",
"plot(t7,phi3t)\n",
"xlabel('t')\n",
"ylabel('phi3(t)')\n",
"show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Example3.2 page 121"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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nB1F8SuJK4OfAyb3u92TGl6aPBK6l+GRIk+KbBXwLuA64GOhr+vHrdCyAE4AT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\nmbU4KZiZWcv/B2PoWc8YAO08AAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7ff3eb2efa90>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Figure 3.8 (b).Representation of transmitted dibits\n",
"Loc. of meg.point| (-3/2)asqrt(T)|(-1/2)asqrt(T)|(3/2)asqrt(T)|(1/2)asqrt(T)\n",
"________________________________________________________________________________\n",
"Transmitted dibit| 00 | 01 | 11 | 10\n",
"\n",
"\n",
"Figure 3.8 (c). Decision intervals for received dibits\n",
"Received dibit | 00 | 01 | 11 | 10\n",
"________________________________________________________________________________\n",
"Interval on phi1(t)| x1 < -a.sqrt(T) |-a.sqrt(T)<x1<0| 0<x1<a.sqrt(T) | a.sqrt(T)<x1\n",
"0.0049504950495\n"
]
},
{
"data": {
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ipptuQmZmpld7f/zxB5o2bYrSpUvjuuuuw5YtW7z68umnn17sS+/evZGRkYGu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\n2rYt//Z4eA6BdaIr4vRp3mMnTrAWRcOGvLfeecf+SnBGWLWKkjRwEZO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"text/plain": [
"<matplotlib.figure.Figure at 0x7ff3eb014210>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from __future__ import division\n",
"from numpy import sqrt,arange,random,sin,pi,zeros,multiply\n",
"%matplotlib inline\n",
"from matplotlib.pyplot import plot,subplot,xlabel,ylabel,title,show,grid\n",
"\n",
"#Signal constellation and Representation of dibits\n",
"a =1# #amplitude =1\n",
"T =1# #Symbol duration in seconds\n",
"#Four message points\n",
"Si1 = [(-3/2)*a*sqrt(T),(-1/2)*a*sqrt(T),(3/2)*a*sqrt(T),(1/2)*a*sqrt(T)]\n",
"plot(Si1,[0,0,0,0])\n",
"xlabel('phi1(t)')\n",
"title('Figure 3.8 (a) Signal constellation')\n",
"grid()\n",
"show()\n",
"print 'Figure 3.8 (b).Representation of transmitted dibits'\n",
"print 'Loc. of meg.point| (-3/2)asqrt(T)|(-1/2)asqrt(T)|(3/2)asqrt(T)|(1/2)asqrt(T)'\n",
"print '________________________________________________________________________________'\n",
"print 'Transmitted dibit| 00 | 01 | 11 | 10'\n",
"print ''\n",
"print ''\n",
"print 'Figure 3.8 (c). Decision intervals for received dibits'\n",
"print 'Received dibit | 00 | 01 | 11 | 10'\n",
"print '________________________________________________________________________________'\n",
"print 'Interval on phi1(t)| x1 < -a.sqrt(T) |-a.sqrt(T)<x1<0| 0<x1<a.sqrt(T) | a.sqrt(T)<x1'\n",
" \n",
"#Implementation of LMS ADAPTIVE FILTER\n",
"#For noise cancellation application\n",
"order = 18#\n",
"t =arange(0,0.01+1,0.01)\n",
"x = [sin(2*pi*5*tt) for tt in t]\n",
"noise =random.rand(len(x))\n",
"x_n = x+noise#\n",
"ref_noise = [noise*xx for xx in random.rand(10)]\n",
"w = zeros([order,1])\n",
"\n",
"\n",
"mu = 0.01*(sum(multiply(x,x))/len(x))\n",
"\n",
"print mu\n",
"\n",
"N = len(x)#\n",
"desired=[]\n",
"for k in range(0,1010):\n",
" for i in range(0,N-order-1):\n",
" if i < len(ref_noise):\n",
" buffer = ref_noise[i]#,i+order-1]\n",
" desired.append(x_n[i]-buffer*w)\n",
" w = w+(buffer*mu*desired[i])\n",
" \n",
"\n",
"subplot(4,1,1)\n",
"plot(t,x)\n",
"title('Orignal Input Signal')\n",
"subplot(4,1,2)\n",
"plot(t,noise)\n",
"title('random noise')\n",
"subplot(4,1,3)\n",
"plot(t,x_n)\n",
"title('Signal+noise')\n",
"show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Example3.3 page 123"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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gzXWjJOm0g4iMBRrE2XRr9BtVVRFJdJoPU9UlIlIXGCsic1U17vo6twqxxOPw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\nmVnJuPCbmZXMf3bsm31O2SeyAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7ff3eaa3f9d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from __future__ import division\n",
"from numpy import arange,sqrt,cos,pi,convolve\n",
"%matplotlib inline\n",
"from matplotlib.pyplot import plot,subplot,title,show\n",
"\n",
"\n",
"fc =4# #carrier frequency in Hz\n",
"T =1#\n",
"t1 = arange(0,0.01+T,0.01)\n",
"phit = [sqrt(2/T)*xx for xx in cos(2*pi*fc*t1)]\n",
"hopt = phit#\n",
"\n",
"phiot = convolve(phit,hopt)#\n",
"phiot = [yy/max(phiot) for yy in phiot]\n",
"\n",
"t2 = arange(0,0.01+2*T,0.01)\n",
"subplot(2,1,1)\n",
"plot(t1,phit)#\n",
"title('Figure 3.13 (a) RF pulse input')\n",
"subplot(2,1,2)\n",
"plot(t2,phiot)#\n",
"title('Figure 3.13 (b) Matched Filter output')\n",
"show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Example3.4 page 124"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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21j77bNiSlM6VV1rpvrvuCluS8sWwYZYnPdfZNFevNjPhgw9a+Od111koaLUQ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\nSik1QDb7lFJqgGz2KaXUANnsU0qpAf4AxwLQj2+z+H8AAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7ff3ea9d83d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from __future__ import division\n",
"from numpy import random,convolve\n",
"%matplotlib inline\n",
"from matplotlib.pyplot import plot,subplot,title,show\n",
"\n",
"\n",
"phit = [0.1*xx for xx in random.uniform(0,1,10)]\n",
"hopt = phit\n",
"phi0t = convolve(phit,hopt)\n",
"phi0t = [yy/max(phi0t) for yy in phi0t]\n",
"subplot(2,1,1)\n",
"plot(range(0,len(phit)),phit)\n",
"title('Figure 3.16 (a) Noise Like input signal')\n",
"subplot(2,1,2)\n",
"plot(range(0,len(phi0t)),phi0t)\n",
"title('Figure 3.16 (b) Matched Filter output')\n",
"show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Example3.6 page 127"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Predictor-error variance 0.64\n",
"1 Predictor input variance 1\n",
"The predictor-error variance is less than the variance of the predictor input\n"
]
}
],
"source": [
"from __future__ import division\n",
"\n",
"Rxx = [0.6, 1, 0.6]\n",
"h01 = Rxx[2]/Rxx[1]# #Rxx(2) = Rxx(0), Rxx(3) = Rxx(1)\n",
"sigma_E = Rxx[1] - h01*Rxx[2]\n",
"sigma_X = Rxx[1]\n",
"print 'Predictor-error variance',sigma_E\n",
"print sigma_X,'Predictor input variance',sigma_X\n",
"if(sigma_X > sigma_E):\n",
" print 'The predictor-error variance is less than the variance of the predictor input'\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Example3.29 page 137"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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jvvvuw4EDB3L05eOPP77el/79+yMlJQWdO3dGyZIl0bFjR2RkZGiOxX//RePo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\nd3FxcalluILfxcXFpZbx/3rGsx341WU1AAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7ff3eadbb110>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from numpy import arange,sin,pi,random,zeros\n",
"%matplotlib inline\n",
"from matplotlib.pyplot import plot,subplot,title,show\n",
"\n",
"\n",
"#Implementation of LMS ADAPTIVE FILTER\n",
"#For noise cancellation application\n",
"\n",
"order = 18;\n",
"t = arange(0,0.01+1,.01)\n",
"x = sin(2*pi*5*t);\n",
"noise =random.random(len(x));\n",
"x_n = x+noise;\n",
"ref_noise = noise*random.random();\n",
"w = zeros(order)\n",
"mu = 0.01*(sum(x**22)/len(x))\n",
"N = len(x);\n",
"subplot(4,1,1)\n",
"plot(t,x)\n",
"title('Orignal Input Signal')\n",
"subplot(4,1,2)\n",
"plot(t,noise)\n",
"title('random noise')\n",
"subplot(4,1,3)\n",
"plot(t,x_n)\n",
"title('Signal+noise')\n",
"show()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.9"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
|