add/update books

1 files changed, 543 insertions, 0 deletions

diff --git a/Wireless_Communications_and_Networking_by_V._Garg/ch3.ipynb b/Wireless_Communications_and_Networking_by_V._Garg/ch3.ipynb
new file mode 100755
index 00000000..40f3b8ea
--- /dev/null
+++ b/Wireless_Communications_and_Networking_by_V._Garg/ch3.ipynb
@@ -0,0 +1,543 @@
+{
+ "metadata": {
+  "name": "",
+  "signature": "sha256:f32dec82dcc091d4a1d388fd0afce868d4917308e897fe0d3ace9d832db79571"
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+  {
+   "cells": [
+    {
+     "cell_type": "heading",
+     "level": 1,
+     "metadata": {},
+     "source": [
+      "Chapter 3: Radio Propagation and Propagation Path-Loss Models"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Example 3.1, Page 51"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import math\n",
+      "\n",
+      "#Variable declaration\n",
+      "hb=100; #in feets(height of BS antenna)\n",
+      "hm=5;  # in feets(height of mobile antenna)\n",
+      "f=881.52;#in MHz\n",
+      "lamda=1.116;  #in feet\n",
+      "d=5000;  #in feet\n",
+      "Gb=10**0.8;  #8dB(BS antenna gain)\n",
+      "Gm=10**0;   # 0dB (Mobile antenna gain)\n",
+      "\n",
+      "#Calculations&Results\n",
+      "free_atten=(4*math.pi*d/lamda)**2*(Gb*Gm)**-1;\n",
+      "y=round(10*math.log10(free_atten));\n",
+      "print 'Free space attenuation is %d dB \\n'%y\n",
+      "reflect_atten= (d**4/(hb*hm)**2)*(Gb*Gm)**-1;\n",
+      "x=round(10*math.log10(reflect_atten));\n",
+      "print 'Reflecting surface attenuation is %d dB \\n '%x"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Free space attenuation is 87 dB \n",
+        "\n",
+        "Reflecting surface attenuation is 86 dB \n",
+        " \n"
+       ]
+      }
+     ],
+     "prompt_number": 3
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Example 3.2, Page 52"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import math\n",
+      "\n",
+      "#Variable declaration\n",
+      "d=8000; #Distance between base station and mobile station\n",
+      "f=1.5*10**9;#in Hz \n",
+      "lamda=0.2;  #in metres\n",
+      "Pt=10; #BS transmitted power in watts\n",
+      "Lo=8;   #Total system losses in dB\n",
+      "Nf=5;    #Mobile receiver noise figure in dB\n",
+      "T=290;   #temperature in degree kelvin\n",
+      "BW=1.25*10**6; #in Hz\n",
+      "Gb=8;  #in dB\n",
+      "Gm=0;   #in dB\n",
+      "Hb=30;  #in metres\n",
+      "Hm=3.;   #in metres\n",
+      "B=1.38*10**-23; #Boltzmann's constant\n",
+      "\n",
+      "#Calculations&Results\n",
+      "Free_Lp=20*math.log10(Hm*Hb/d**2);\n",
+      "Pr=Free_Lp-Lo+Gm+Gb+Pt;  #in dBW\n",
+      "Te=T*(3.162-1);\n",
+      "Pn=B*(Te+T)*BW;\n",
+      "print 'Received signal power is %d dBW \\n'%(10*math.log10(Pn))\n",
+      "SNR=Pr-10*math.log10(Pn);\n",
+      "print 'SNR ratio is %d dB \\n'%(round(SNR))"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Received signal power is -138 dBW \n",
+        "\n",
+        "SNR ratio is 31 dB \n",
+        "\n"
+       ]
+      }
+     ],
+     "prompt_number": 5
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Example 3.3, Page 58"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import math\n",
+      "\n",
+      "#Variable declaration\n",
+      "d=3*1000;#in metres\n",
+      "Y=4;# path loss exponent\n",
+      "Pt=4;  #Transmitted power in watts\n",
+      "f=1800*10**6;#in Hz\n",
+      "Shadow=10.5;  #in dB\n",
+      "d0=100.;#in metres\n",
+      "P0=-32;   #in dBm\n",
+      "\n",
+      "#Calculations&Results\n",
+      "print \"Using equation 3.11 and including shadow effect we get\"\n",
+      "Pr=P0+10*Y*math.log10(d0/d)+Shadow;\n",
+      "print 'Received power is %.1f dBm \\n'%Pr\n",
+      "path_loss=10*math.log10(Pt*1000)-Pr;\n",
+      "print 'Allowable path loss is %.1f dB \\n'%path_loss"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Using equation 3.11 and including shadow effect we get\n",
+        "Received power is -80.6 dBm \n",
+        "\n",
+        "Allowable path loss is 116.6 dB \n",
+        "\n"
+       ]
+      }
+     ],
+     "prompt_number": 8
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Example 3.4, Page 58"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "#Variable declaration\n",
+      "shadow=10.; #in dB\n",
+      "Lp=150;  #in dB\n",
+      "\n",
+      "#Calculations&Results\n",
+      "print \"Using equation given in Problem i.e Lp=133.2+40*math.log(d) we get,\"\n",
+      "d=10**((Lp-10-133.2)/40);\n",
+      "print \"Separation between transmitter and receiver as %.2f km\"%d"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Using equation given in Problem i.e Lp=133.2+40*math.log(d) we get,\n",
+        "Separation between transmitter and receiver as 1.48 km\n"
+       ]
+      }
+     ],
+     "prompt_number": 10
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Example 3.5, Page 61"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import math\n",
+      "\n",
+      "#Variable declaration\n",
+      "v=60*0.44704;  #.. mph to mps\n",
+      "fc=860*10**6;#in Hz\n",
+      "td=2*10**-6; #RMS delay spread in sec\n",
+      "c=3.*10**8;# speed of light in m/sec\n",
+      "Rs=19200.; #Coded symbol rate in bps\n",
+      "\n",
+      "#Calculations&Results\n",
+      "lamda=c/fc;\n",
+      "fm=v/lamda; #Maximum doppler shift\n",
+      "tc=1/(2*math.pi*fm);#Channel coherence time\n",
+      "print 'Channel coherence time is %.4f sec \\n'%tc\n",
+      "ts=1/Rs; #symbol interval\n",
+      "print 'Symbol interval is %d microsec \\n'%(ts*10**6);\n",
+      "print \"As the symbol interval is much smaller compared to the channel coherence time. So, Symbol distortion is minimal and fading is slow.\";\n",
+      "print \"\";\n",
+      "Bc=1/(2*math.pi*td);\n",
+      "print 'Coherence Bandwidth is %.2f kHz \\n'%(Bc/1000)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Channel coherence time is 0.0021 sec \n",
+        "\n",
+        "Symbol interval is 52 microsec \n",
+        "\n",
+        "As the symbol interval is much smaller compared to the channel coherence time. So, Symbol distortion is minimal and fading is slow.\n",
+        "\n",
+        "Coherence Bandwidth is 79.58 kHz \n",
+        "\n"
+       ]
+      }
+     ],
+     "prompt_number": 11
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Example 3.6, Page 65"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import math\n",
+      "\n",
+      "#Variable declaration\n",
+      "p=1;# re\ufb02ection coef\ufb01cient of ground \n",
+      "c=3.*10**8;# velocity of light in free space(m/sec)\n",
+      "e=2.71828;#Euler's number\n",
+      "fm=20;  #in Hz\n",
+      "fc=900*10**6; #carrier frequency in Hz\n",
+      "\n",
+      "#Calculations&Results\n",
+      "Nr=math.sqrt(2*math.pi)*fm*p*e**-(p**2);\n",
+      "print 'NO of fades per second are %.2f  \\n'%Nr\n",
+      "Afd=e**-(p**2)/(p*fm*math.sqrt(2*math.pi));\n",
+      "print 'Average fade duration is %.4f sec \\n '%Afd\n",
+      "v=fm*c/fc;\n",
+      "print 'Maximum velocity of mobile is %.2f m/sec = %d Km/hour \\n'%(v,v*18/5);"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "NO of fades per second are 18.44  \n",
+        "\n",
+        "Average fade duration is 0.0073 sec \n",
+        " \n",
+        "Maximum velocity of mobile is 6.67 m/sec = 24 Km/hour \n",
+        "\n"
+       ]
+      }
+     ],
+     "prompt_number": 13
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Example 3.7, Page 70"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import math\n",
+      "import matplotlib\n",
+      "import matplotlib.pyplot as plt\n",
+      "import numpy as np\n",
+      "%matplotlib inline\n",
+      "\n",
+      "#Variable declaration\n",
+      "d=np.array([1, 2, 3, 4, 5]);  #in km\n",
+      "hb=30; #Height of BS antenna in metres\n",
+      "hm=2;# height of mobile antenna in matres\n",
+      "fc=900;#carrier frequency in MHz\n",
+      "W=15;  #street width(m)\n",
+      "b=30;  # distance between building along radio path (m) \n",
+      "phi=90; # incident angle relative to the street\n",
+      "hr=30; #in m\n",
+      "\n",
+      "#Calculations\n",
+      "dellhm=hr-hm;\n",
+      "#L50=Lf+Lrts+Lms\n",
+      "\n",
+      "# By COST 231 model\n",
+      "Lf=32.4+20*np.log10(d)+20*np.log10(fc);\n",
+      "L0=4-0.114*(phi-55);\n",
+      "Lrts=-16.9-10*math.log10(W)+10*math.log10(fc)+20*math.log10(dellhm)+L0;\n",
+      "Lbsh=-18*math.log10(11);\n",
+      "ka=54-0.8*hb;\n",
+      "dellhb=hb-hr;\n",
+      "kd=18-15*dellhb/dellhm;\n",
+      "kf=4+0.7*(fc/925-1);\n",
+      "Lms=Lbsh+ka+kd*np.log10(d)+kf*np.log10(fc)-9*np.log10(b);\n",
+      "L50=np.array([0, 0, 0, 0, 0])\n",
+      "L50=Lf+Lrts+Lms;\n",
+      "\n",
+      "#Okumura/Hata model\n",
+      "ahm=(1.1*math.log10(fc)-0.7)*hm-(1.56*math.log10(fc)-0.8);\n",
+      "L_50=69.55+26.16*np.log10(fc)+(44.9-6.55*np.log10(hb))*np.log10(d)-13.82*np.log10(hb)-ahm;\n",
+      "L_50 = np.array(L_50)\n",
+      "\n",
+      "#Results\n",
+      "fig,ax1 = plt.subplots()\n",
+      "ax1.plot(d,L_50,'b-')\n",
+      "ax1.set_xlabel('Distance from transmitter(in km)')\n",
+      "ax1.set_ylabel('Path loss (in dB)')\n",
+      "ax2 = ax1.twinx()\n",
+      "ax2.plot(d,L50,'r')\n",
+      "ax1.legend(['COST 231 model'],loc=0)\n",
+      "ax2.legend(['HATA model'],loc=0)\n",
+      "ax1.grid()\n",
+      "plt.show()\n",
+      "print \"L50 values by Cost 231 model\"\n",
+      "print '%.2f  %.2f  %.2f  %.2f  %.2f \\n '%(L50[0],L50[1],L50[2],L50[3],L50[4]);\n",
+      "print \"L50 values bu Okumura/Hata model\"\n",
+      "print '%.2f  %.2f  %.2f  %.2f  %.2f \\n '%(L_50[0],L_50[1],L_50[2],L_50[3],L_50[4]);\n",
+      "print \"The results from the plot of two models shows that the calculated path loss with the COST 231 model is higher than the value obtained by the Okumura/Hata model.\"\n",
+      "\n",
+      "#Answers vary due to built-in functions of Python used"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAZYAAAEPCAYAAABhkeIdAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJztnXd4VNXWh1+KNCkJUqRHBMEGiIUugU+6AlIEFDTCBTWK\nCl4LKk1RAQW8oIgiCAQBQZCO9NAE6R1pCtJ7h0DK+f5YZ5LJZGYyCdNOst7nOU9OP7/sTM6avdbe\na4GiKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIrfGQucAnbY7esHHAW2mEsTu2O9gP3A\nX0BD/0hUFEXJlDh7PwN0B/YAO4FBdvuD5v1cB3iE5ML7Aj2dnPsAsBW4AwgDDgBZfaxPURQls+Ls\n/VwPWIy8hwEKmz/T/H725ct7FXDByf4sTva1ACYDscAhRPgTPlOmKIqSuXH2fn4N+AJ5DwOcMX+m\n+f0ciF5Bd2AbMAYIMfcVR1xkNo4CJfysS1EUJTNTHngSWAdEA4+Z+9P8fva3YfkOuAeoApwAhrg5\n1/CLIkVRFAUgOxAKVAfeBaa6Odft+zm7F0V5wmm79R+BOeb6MaCU3bGS5r5kFC9e3Dh+/Ljv1CmK\nomRMDgLlUjnnKDDDXN8AJACF8PD9bI+/eyzF7NafJSlwNBtoD+RAejTlgfWOFx8/fhzDMIJ+6du3\nb8A1qE7VqTpV4+nTBsOHGwD3evB+ngnUN9fvM9/HZz19P9vjyx7LZKAuYvGOICPCwhE3mAH8A7xi\nnrsb6XbtBuKASCzsCjt06FCgJXiE6vQuqtO7WEFnMGqMiYHZsyEqClatgqefdnqa7f18F/J+7oMM\nQR6LfOG/Bbxonpvm97MvDUsHJ/vGujn/c3NRFEVR0kBCAqxeLcZk+nR49FHo1AkmT4a8eeHnn1Nc\n4uz9DNDJxf40vZ/9HWPJFERERARagkeoTu+iOr2LFXQGWuPevWJMJk6EfPnEmGzfDiVLBlSW0zkl\nwYxhGJb1kCmKotw2Z8/ClCliUP79F55/XgxK5cqQxcUbPYscsNr73m8YzggNDTUQn58uuqRpCQ0N\ndfqZSg/Lly/32r18iTOd+j+UMRZXn2fzuN/IEK6wCxcuYGhPRkkHWVx9xctk6P9QxiBYPs/BocJz\nDGcf/ixZsug/hZIu9LMjaDtkDFz9Hf3tCtNEj4qiKIpXUcOiKF4iOjo60BI8wio6FeuihkWxLOPG\njaNOnToenRsREUHv3r19rEhR0k9aPqNhYWEsXbrUx4rSjxoWPzFp0iQee+wx8uXLR/HixWnatClr\n1qxJPL57926aN29OSEgI+fPnp379+qxduzbZPcaMGcP9999P/vz5ufvuu2nWrBlXr16lSZMm5MuX\nj3z58pEjRw5y5syZuB0ZGZlCy7x586hduzahoaEUK1aMrl27cvXq1cTj7733HqVLlyZ//vyULFmS\nnj17EhcXl3i8W7duVKxYkWzZsjF+/HgftJb3yZIli88Dm+Hh4T69v7ewik4bzl6irr5UhIeHU7Bg\nQWJjJfP7q6++mvi/kDNnTnLkyJG43axZMwAMw6Bs2bI8+OCDvv9l3JCWz6g/Ps+3gxoWPzB06FB6\n9OjBxx9/zOnTpzly5Aivv/46s2fPBuDgwYPUqlWLypUrc+jQIU6cOMGzzz5Lw4YNWbduHQArVqzg\no48+YsqUKVy+fJk9e/bQvn17ABYsWMCVK1e4cuUKL7zwAu+//37i9siRI1PouXz5Mn369OHEiRPs\n2bOHY8eO8e677yYe79KlC7t37+by5cusX7+eRYsW8eOPPyYer1KlCiNHjqRq1apB/eF2RIPT1sTT\nl+ihQ4dYv349RYoUYdasWQCMGjUq8X/hww8/pH379onb8+bNA2DlypXcvHmTM2fOsHHjRp/+LqmR\nUT6jalh8zKVLl+jbty8jR46kZcuW5M6dm2zZstGsWTMGDZLKn/369aNWrVp8+umnhISEcOedd9K9\ne3c6derE+++/D8CGDRuoUaMGlStXBiA0NJROnTqRN2/eFM9M7cPZoUMHGjZsSK5cuQgJCaFr167J\nek8VKlRIvK9hGGTNmpVixZLyh0ZGRlK/fn1y5cqV6u8fERFBZGQkTZs2JV++fNSpU4eTJ0/y1ltv\nERoayv3338/WrVsTz9+zZw/h4eGEhoby0EMPMWfOnMRj586do3nz5hQoUIBq1apx8ODBZM/666+/\naNCgAXfddRcVK1Zk2rRpqerzJlaJXVhFpzucGZoJEybw1FNP0alTJ6c9acNMzOjI+PHjad26NS1a\ntEi1Bx4WFsZXX31FpUqVyJcvH126dOHUqVM0adKEAgUK0KBBAy5evJh4/uzZs3nwwQcJDQ2lXr16\n/PXXX4nHtmzZQtWqVcmfPz/t27cnJiYm2bPmzp1LlSpVCA0NpVatWuzY4VhFOHhRw+Jj1q5dS0xM\nDM8++6zLc5YsWULbtm1T7G/bti1r1qzh5s2bVK9enYULF9KvX7/Efd5ixYoVPPTQQ8n2DRw4kHz5\n8lGqVCmefvppWrRoke77T5s2jc8++4yzZ8+SI0cOqlevzuOPP8758+dp06YNPXtKterY2FieeeYZ\nGjduzJkzZxgxYgQvvPAC+/btA+D1118nT548nDx5krFjx/LTTz8lvmCuXbtGgwYN6NixI2fOnGHK\nlClERkayZ8+edOtWggdHg+DMQEyYMIF27drx3HPPsXDhQk6fPp3iHEeuX7/O9OnTE6+bMmVKohvN\nGVmyZGHGjBksXbqUvXv3MnfuXJo0acLAgQM5ffo0CQkJDB8+HIB9+/bx/PPPM3z4cM6ePUvTpk15\n5plniIuL49atW7Rs2ZKXXnqJCxcu0LZtW6ZPn574ed6yZQtdunRh9OjRnD9/nldeeYXmzZu71aak\nH5ezSlMDvLOklYkTJxp3332323OyZ89uLFy4MMX+PXv2GFmyZDGOHz9uGIZhLFiwwHjmmWeMkJAQ\nI2/evEbPnj2N+Pj4ZNdEREQYH3/8scf6Fi1aZISGhhr79+93enzz5s1G6dKljenTp6c4Vrt2bWP8\n+PFu7x8REWF069YtcXvEiBHGAw88kLi9fft2IyQkxDAMw1i5cmWKturQoYPRr18/Iy4uzrjjjjuM\nvXv3Jh778MMPjdq1axuGYRhTpkwx6tSpk+zabt26Gf3790/U4axdPPnsZAZSbYdA/QMZhlGmTBkj\nb968RkhISOKSJ0+eZH/vVatWGbly5TIuX75sGIZhVK5c2Rg2bFiy+/Tt29fo2LFjsn1RUVFGyZIl\nDcMwjLi4OKNQoULGb7/95lJLWFiYMWnSpMTt1q1bG5GRkYnbI0aMMFq2bGkYhmF88sknRrt27RKP\nJSQkGCVKlDCio6ONFStWGMWLF09275o1axq9e/c2DMMwXn311cR1GxUqVDBWrlyZqGPp0qUp9Ll7\nR/rzRZ1peize+s9IK3fddRdnz54lISHB5TmFChXCWQGzEydOkDVrVkJDQwFo3Lgxs2fP5sKFC8ya\nNYtx48Yli32klXXr1vHCCy8wffp0ypVzXgPokUceITIykqioqHQ/p0iRIonruXLlSradO3fuxIED\nx48fp1SpUsmuLVOmDMePH+fs2bPExcUlO166dOnE9cOHD/Pnn38SGhqauEyaNIlTp06lW7diR6D+\ngZBewqxZs7hw4ULiMnLkyGS9lvHjx9OwYUPy5csHSG/fk4El48ePp1WrVgBky5aNli1bpnpd0aJF\nE9dz586dbDtXrlzJPs/2n9EsWbJQqlQpjh07xokTJyhRInl13zJlyiSuHz58mCFDhiT7PB89etTp\neyIYyRApXYKZGjVqkDNnTn777Tdat27t9JynnnqKadOmpciUOnXqVGrWrOk0llG/fn3q16/Prl27\n0qVry5YttGjRgnHjxlGvXj2358bGxnLnnXem6zlpoXjx4hw5cgTDMBJdAocPH6ZixYoULlyY7Nmz\n8++//1KhQgUA/v3338RrS5cuTd26dVm0aJHPdboiOjraEiOurKLTHfZG5caNG0ydOpWEhITEWODN\nmze5ePEi27dvp1KlSkDKuMzRo0dZtmwZGzZsYOpUqcJ7/fp1YmJiOHfuHHfddVeatdhTokSJZHER\nwzA4cuQIJc3Uw8eOJS/CePjw4cQveKVLl+ajjz7iww8/9EhDsJFpeiyBokCBAnzyySe8/vrrzJo1\ni+vXrxMbG8uCBQsSA/N9+/bljz/+4OOPP+bChQtcuXKFESNGEBUVlRjgnz17Nr/88ktiTqf169ez\nYsUKqlevnux5rj7k9uzcuZPGjRvzzTff0LRp0xTXf//991y8eDHxOSNHjkz8VgdiaGJiYkhISODW\nrVvExMS4fK4nemxUq1aNPHnyMHjwYGJjY4mOjmbu3Lm0b9+erFmz0qpVK/r168eNGzfYvXs348eP\nT3xZNGvWjH379jFx4kRiY2OJjY1lw4YNicHStOhQrMXMmTPJnj07e/bsYdu2bWzbto09e/ZQp04d\nJkyYkHie42cgKiqKihUrsm/fvsTr9u3bR8mSJZk8efJt62rbti3z5s1j2bJlxMbGMmTIEHLlykXN\nmjWpXr062bNnZ/jw4cTGxjJjxgw2bNiQeG3Xrl0ZNWoU69evxzAMrl27xrx585JNCwhm1LD4gZ49\nezJ06FAGDBhAkSJFKF26NCNHjkwM6JcrV47Vq1ezbds2wsLCKF68OL/99huLFi2iRo0agIwCGz16\nNPfddx8FChSgU6dOvPfee3TokLxejydDM4cOHcq5c+fo3Llz4pj+hx9+GJB/vpkzZ3LvvfdSoEAB\nunTpwoABA5IZlgYNGpAnTx7WrVtHt27dyJMnD6tWrXL6LEc9zvTZtnPkyMGcOXNYsGABhQsX5o03\n3iAqKor77rsPgG+++YarV69y991307lzZzp37px4j3z58rFo0SKmTJlCiRIlKFasGL169eLWrVse\nt8vtYpVegFV0usP+7zlhwgQ6d+5MyZIlKVKkCEWKFKFo0aK88cYbTJo0KdEN7fgZmDBhApGRkYnX\n2K579dVXkxkkT7Q401WhQgUmTpxI9+7dKVy4MPPmzWPOnDlkz56dHDlyMGPGDMaNG8ddd93F1KlT\nk3k0Hn30UUaPHs0bb7xBwYIFKV++PBMmTLDM8H5rqEzCcPbNUxPoKelFPzuCtkPGQJNQKkoGwyrz\nQ6yiU7EualgURVEUr6KuMCVTo58dQdshY6CuMEVRFCVDooZFUbyEVWIXVtGpWBc1LIqiKBmRK1dg\n1ix47TVnR8cCpwD7zJb9gKPAFnNpbO4PA27Y7U+ZMt2BDDHzPjQ01DLju5XgwpYuxxtYZX6IM536\nP5QxCC1QAAYNgt9/h40boXp1aNTI2ak/ASMA+wk7BjDUXBw5ADziqY4MYVjOnz8faAmKYmms8D90\n6RJMmwZRUbB7Nzz3HHTqBNWqQaa1iefOweLFYkgWLoS8eeHoUfjvfyE8HGypmOzqLZmsQnoijnil\nJdUV5gOs4sNWnd5FdXqX6OhoYmNh7lxo1w7KlIEFC6BHDzh2DL79Vr6QB9Ko+L0t4+Nh7Vro21cs\natmyMGkSPPEErF4N+/fDiBHQrFmSUUkb3YFtwBggxG7/PYgbLBqondpNMkSPRVGUjMXmzfJ+bNcO\nypWDF1+E776DggUDrSwAHD8uvZHff4clS6BkSWjcGL74AmrVgpw5U1wSHR2dHqP3HfCJuf4pMATo\nAhwHSgEXgKrATOBB4IqrG1mtA+l0HouiKNbHMOT9OXAgHDoEL78MHTvCvfcGWpmfuXkT1qwRQ/L7\n7+LaatBAjEnDhuCQbt8TXMxjCQPmAA87ucTdseXAO8BmV8/THouiKAElLg5+/VUMSnw8fPCB9FSy\nZ6a308GDSXGSFSvg/vvFkHz/PTz+uL8aoxhwwlx/lqQRY4WQ3ko8UBYoD/zt7kYaY/EBVvJhWwHV\n6V2CRWdMjLw3K1SQeMlnn8H27fDCC/IeDRad7ki3xmvXYN486N4dypeH2rVhwwZ4/nn4+29Ytw76\n9YMaNXxlVCYDfwAVgCNAZ2AQsB2JsdQFepjnPmnu2wJMA14BLrq7eWb6TqAoShBw6RKMGgVffw2P\nPgrjx8t7NUNjGLBrV5J7688/4bHHpFfy669QqZK/RyF0cLJvrItzZ5iLx2iMRVEUv3DyJPzvf/DD\nD9CkCbz3nrxPMywXLsDSpUnG5I475Bdv3Bjq1QOzjLI/8HeuMO2xKIriU/7+G776CqZMEU/Pxo1w\nzz2BVuUDEhJg06YkQ7J9O9SpI4bkvffE5ZVJJtxojMUHWME3DKrT26jO5GzbJobkiScgNBT++gu+\n+cZzo2KF9vxjxgyYMEECQ0WLQkQEXLwo8ZEzZ2D+fHjzTbjvvkxjVEB7LIqieBHDkHl6AwfCli0y\nmXHUKMifP9DKvERsrExQNHslj+/fLylTGjWSeSWlSwdaYVDgSxM6FmgGnCblWOh3gC+RYWznkTHT\ne4C/zONrgUgn99QYi6IEIQkJMshp4EA4fVo8P506Qa5cgVbmBQ4dSpqguHy5zNhs3FiWatUkdhLk\nZKQYi7MkZyAzOBsAhx32pynJmaIogSc2VmIngwZBjhwyB6V1a8iWLdDKboMbN2Quic2YnDsnPZI2\nbWR8dJEigVYY9PgyxrIKmVTjyFDgPR8+N+BYwTcMqtPbZCad169LvKR8efjpJxg6VOLWzz3nPaPi\nt/Y0DAkAff219EKKFIHPP4dChWDiRBnOFhUlcRQHo2KVv7m/8XeMpQWS73+7k2O2JGeXgI+B1X7U\npSiKB1y4IJMZR4yAmjWlt1K9eqBVpYPLl5OGAi9cKL68xo2hWzf45RcoUCDQCi2NPw1LHuBDxA1m\nw+bz8zjJWUREBGFhYQCEhIRQpUqVxPoStm8Puu3Ztm1fsOix+rZtX7Do8eb28ePQo0c08+dDmzbh\nREfDqVPRxMQA+Ob5tn1euV9CAhvHjKHg+vWU3bcPNm/mfMWKnH/8ccrNnw/330/0ihVyvmlUPL2/\nvVZv/v6+aE9/4etgThhJicweBpYA181jJYFjwBNIgN8eV0nONHivKH5k3z748kuYPh1eegl69oRS\npQKtykPOnEleqyQkJCnoXrcu5MkTaIV+w9/Be3/OY9kBFEVcXvcgLrGqiFEpBNg8sx4lOQtmHL/J\nBCuq07tkJJ2bNkHbtpKVvUQJMTDDhvnXqKS5PePiJCtwnz4yeaZcOZg6VXx269bB3r0y9b9JE68Z\nFav8zf2NL11hk5FEZnchSc76ICPFbNh3PZ5E6gDEAgl4kORMURTvYhgymvaLLySW/c47EpjPmzfQ\nytxw9GjS6K2lS6UaWKNGMHiwGJQcOQKtMFNitamg6gpTFC+TkAAzZ8oclCtX4P33ZcZ8UL6Tb96E\nVauS3FsnTiSvVVKsWKAVBiX+doWpYVGUTMqtWzKadvBgmRnfqxe0aAFZgy3R04EDSfm3Vq6Ehx4S\nQ9KokWQItvSkGf+QkWMsmQar+F1Vp3exis4FC6IZNkwqM/7yi5T8/fNPePbZIDEqt27B3Lkca9lS\nRD75pNQqfvFFmQX/xx8SR6lWLeBGxSp/c3+jucIUJZNw9qzMP/n6a/myP2sWVK0aaFUmhiE5uCZO\nlID7/fcT8+CD8Omn0kPJRAkcMwJW+2upK0xR0si//8rM+AkTJCvJu+/KjPmgYP9++PlnMSjZs0uC\nsRdeAHOumuIdMlKuMEVRAsju3RI/mTMHunSBnTuhePFAq0K6Tr/8Isbk77+hQweZwv/oo9ozySAE\ng0c1w2EVv6vq9C7BonPdOmjZUooUli8vse/Bg5OMSkB0xsTAtGnQvLnML1mzBnr3luHCX38tQXgH\noxIs7ekOK2gMBNpjUZQMgGHAokUyZPiff8TdNWlSgCeXJyTI0OCoKJgxQwI6nTpJTyXDFGhRnGG1\nfqfGWBTFjvh4+PVXMSixsZK2vl27AJcI2bNHjMnPP0syx06dxN1VsmQARWVuNMaiKEqqxMTA+PGS\nx6toURk81bRpAIcLnzwpcZKoKFl//nkJ7lSqFCBBSiDRGIsPsIrfVXV6F3/ovHxZ4iVly8p7e9w4\nCVc8/bTnRsVrOq9dE39bkyZQsaLUIh40SIahffnlbRsVK/zdraAxEKhhURQLcOoUfPihGJRt22QS\n+ty5ULu2n4XEx8OSJZLquGRJ6aF06gTHjkkX6qmnAj5pUfGIscApJDmwjX5IcuAt5tLE7lgvYD9S\nPr5hajfXGIuiBDH//CNf/qdMkTDFO++IcfE727ZJ0H3SJMnH1bEjtG8Pd98dADFKWnESY6kDXEVK\nxz9s7uuL1MAa6nD5A8Ak4HGgBFL+5D4kYbBTNMaiKEHI9u3iVVq4EF55ReLhRYv6WcTRo2JIJk6E\nS5dk4uLixfDAA34WoviAVUi9LEecdTZaINnqY4FDwAGkjtY6VzdXV5gPsIrfVXV6F2/oXLUKmjWT\nHIuVK8PBg/DZZ941Km51XrkigZunnpIYyf79kgfmn3+kDrwfjYoV/u5W0JhGugPbgDFAiLmvOOIi\ns3EU6bm4RHssihJgEhJg/nwZMnzyJLz3nlRszJXLTwLi4mQSzMSJIqRuXekmPfOMH0Uo3iQ6Ojo9\nRu87pC4WwKfAEKCLi3PdxiQ0xqIoASI2VjKbDBokabI++EByefkl9m0YUiYyKkoCOGXLShD+ueeg\nUCE/CFD8iYt5LGEklY53xP7YB+a+gebP35F4zJ+unqc9FkXxM9evw9ix8NVXcM898rNhQz+lyTp0\nKCnpY2ysBOFXrw6irJRKACkGnDDXnyVpxNhsJHg/FHGBlQfWu7uRxlh8gFX8rqrTu6Sm88IFiZeU\nLStVdKdMkVLAjRr52KhcvAijR0tdk8ce49j69WLZ9u+Hfv2C1qhY4e9uBY0umAz8AVRASsd3BgYB\n25EYS12gh3nubmCq+XMBEEkqrjDtsSiKjzl+HIYNk3f5M8/AsmV+iIHfugULFoira/FiKd/7zjvQ\npAn7//iDEjVq+FiAEuR0cLJvrJvzPzcXj9AYi6L4iP37ZQ7Kr79K8cOePaF0aR8+0LFY1gMPSNyk\nTRsIDfXhg5VgR3OFKYrF2bRJAvLLl0NkJOzb5+N4uLNiWRs3arEsJWBojMUHWMXvqjq9h2HAsGHR\nNGwILVpAjRoy9aN/fx8ZlbNn4dtv5UG1a0scZcoUmUn50UdujYoV2hOsodMKGgOB9lgU5TbZvx+6\ndpViiP37ywT1HDl88KCYGMk8GRUFK1bITMrevSV+EtA8+YqSnLT43HIhIwFu+kiLJ2iMRQka4uPh\nf/+TCem9e8Mbb/hgDoqzYlkdO0KrVlosS/GYYIqxZAVaIqMHaprbWYB4YC3wMzCTVIadKUpG5K+/\noHNn6SisWyfVdr2Ks2JZ27drsSzFEriLsUQDjwJfAWWRyTN3m+tfIZkuV/hYnyWxit9VdaaduDip\nh1K7tri8li9PMiq3rfPkSan//uijkqsrNlZcX9u3S61hLxmVYGpPd1hBpxU0BgJ3PZYGOHd73USy\nWq4DcvpClKIEI7t2wcsvQ758sGGDzJq/ba5dg1mzpHeydq1E/gcNgnr1tK6JYlnS4nO7E7gfOAyc\n8Y2cVNEYi+J34uJkPsrQoTBgAHTrdpsz5ePjpasTFQWzZ0P16uLqatEC7rzTa7oVxUYwxViaA8OB\n88DHwLdIxbF7gPeBcb4WpyiBZscO6aUULChTQ8qUuY2bOSuWNWiQFstSMhzuYiwDkBKUrwDTgP8D\nqiPZLv/re2nWxSp+V9XpmthY+PRTqF8fXn1VCm6lZlSc6jx6VIIylSpB8+YS7V+8WKzU228HxKjo\n3917WEFjIHDXY4kH9pnrf5sLwGmkkpiiZEi2bpVeSrFisHkzlCqVxhtcuSIFVSZOlBu0aiXFsurU\ngaw6J1nJ+LjzuW0Hws1zlpvr2G1X8qUwF2iMRfEZt25J9uHvvpNOxksvpSGW4qxYVseOWixLCQqC\nKcaSH9hkrmexW1eUDMemTdJLKVNGeizFi3t4YWyslPIdMEC6OJ06wfDhWixLydS465eHIYH6exzW\nbYviAqv4XVUn3LwpqbWaNpWSwLNne2hU4uLEoFSoIJmEp0wheuBAeP31oDcq+nf3HlbQGAjc9Viq\npnLtZm8KURR/s3699FLuu08GbHkUR4+Pl2SP/fuLBRo3TgpoAehLRlEA9z63aCRdS25kBv52c38l\nYCOQWqWgsUAzJNjvWFP5HeBLoBAynBmgF1LFLB54E1jk5J4aY1Fum5gY6NsXxo+Xie7t2nkQS0lI\nkIB8v36SYsU2ZMwv9YQV5fYIphhLuPlzBtCVpPrHDwH9Pbj3T8AIYILD/lLIrP7DdvseANqZP0sA\nS4D7gAQPnqMoHrN2rfRSKlWSTClFiqRygWHAzJliiXLlgiFD/FBLWFGsjSdjHyuSZFQAdiIz8FNj\nFXDByf6hwHsO+1ogNZhjgUPAAeAJD54RlFjF75qZdF6/LpV5W7WSOPvUqakYFcOAuXMlb9cnn8hw\nsT//hMaNXRqVzNSe/sAKOq2gMRB4Uo9lO/AjMBHpSj0PbEvn81oAR0lyq9kojuQes3EU6bkoym2z\nahV06SI2YseOVGLrhiHDhvv0EWvUvz+0bKnzTxQlDXjSn88NvAbUMbdXAt8BMR5cGwbMQWIseZD5\nLw2Ay8A/wGPAOcRltg5JxQ9iyOYjbjh7NMaieMy1a/DhhzBtGowcKfbBLcuWiUE5d04MSps2alCU\nDEEwxVhs3EDcV0Nv81n3IobG1tspicyNqQYcQ2Iv2B075uwmERERhJllV0NCQqhSpQrh4eFAUrdU\nt3V7xQp4/vloHngAdu4Mp2BBN+dnzw69e3N9/34Ov/QS93/yCWTLFlS/j27r9u1uZyTCSB6fsecf\noKC5/gCwFciBzJE5iHPraliB5cuXB1qCR2REnVeuGMbrrxtGiRKGMWdOKievXWsYDRoYRliYYYwd\naxixsX7TGUhUp/ewgkbDMAz8XJDRl/38ycAfyOiuI8DLDsftf9HdwFTz5wIgEq1MqaSRpUvh4YfF\nBbZjBzyAM/t9AAAgAElEQVT9tIsTN22SevHPPSfurr17ZahYdk868IqSIRiLZKt39sX/HWREru2L\nfxjiudpiLiNTu7nVxkyaxldRkrh8WWbNz5sHP/wATZq4OHHbNhk2vGGDBF/+8x/IqbXqlIyPkxhL\nHeAqMh3Efp5hKWA0UAGZv3ie5LFyj/Ckx1LBfNBiJPi+HFjm6QMUxZcsWiS9lPh42LnThVHZtQva\ntpWhwuHhcOCApF5Ro6JkXtIyHSTNeGJYpiHpWz4G3rVbFBfYgmbBjpV1XrokHY6uXWH0aFkKFHA4\nae9eeP55mSH/xBNiUN5+G3Ln9pvOYER1eg8raEwDrqaDgMS+tyAZWWqndiNPnMqxyPBiRQkK5s+H\nV16RGMqOHZA/v8MJBw/KpMb586FHD/j+eylUryiZhOjo6LQavTzAh8h0EBs219lxxEV2AckhORN4\nELji6maexFj6ITXuZwA37fafd3q2b9EYSybmwgWxEytXwo8/SkckGYcPSw6vmTOhe3fpnaToxihK\n5sPFPJYwkmInDyOptK6bx2xTPp5A8j3asxwJ8LtMROxJjyUCGaHlWI5YU+crfmP2bHjtNUnJsn07\n5M1rd/DoUUm5MnWqnLRvnxSpVxTFU3YARe22/yEpeF8I6a3EA2WB8iRVFHaKJzGWMFLWYlGj4gar\n+F2toPPcOWjQIJqePWHSJKnwm2hUTpyAN9+UjJL580tMZcCAgBkVK7QnqE5vYgWNLkjLdJAnkYnt\nW5CY+yvARXc3d9dj+T9gKdAa53NKHNOtKIpXmTED3ngDataUkcJ33mkeOH0aBg2Cn36CiAjYsweK\nFnV3K0VRktMhleNl7dZnkMb3vbsYS3+gLzAO54bF0cL5A42xZALOnJEQyebNYjtq1TIPnDsHX34p\nQ8Cefx569UpDDWFFybz4O1eYTpBUgopp08S71bGjDOzKnRuJ2g8dKpkk27aVyY2lSwdaqqJYBn8b\nFncxlgjcu8pyEJheS9BjFb9rMOk8fVpsRp8+8Ntv0jHJHXsZPvmE2LAwOH4cNm6EUaOC1qgEU3u6\nQ3V6DytoDATuDEteYAMS5OmJ1GF5ARlmNhn4E0mpryjpxjBg8mSJv5crB1u2QPWHrsIXX8iOAwfY\n/O23MGYM3KNjRhTFCqTWNcoC1EJmWtq+Jh4GViMjCvztl1JXWAbi5EkZHbx/v8RSHn/wuri7vvwS\n6tWTvF73e1KsVFEUdwRbPRYDMSKr/aBFySQYBkycCP/9r6RkmTIuhpzjf4AWA6FGDViyRBKAKYpi\nSbQ8ng+wit81EDqPHYPmzaVTsmDmTQaU+I6cD5UXYzJvHkyfnsKoaHt6F9XpPaygMRCoYVH8gmGI\nu+uRR+DxKrFsjvyRqh0qwJw5MmFl9mw5qCiK5dHhxorPOXIEunWDMyfimNFqIqXHfQL33it15WvW\nDLQ8RcnwBNNwYxtvAwUQUWOQaf2NfClKyRgYhsxlfOyReF7J+zMbrj1A6aU/Sddl8WI1KoqSQfHE\nsHQGLgENkVKVnYCBvhRldazid/WlzsOHoXHDBA4MnMbhkEq0PPYtWUZ9B9HRULdumu6l7eldVKf3\nsILGQOBJdmNb96kZEAXs9J0cxeokJMD3owzWfjCLqDv7UrhEDrIMGAKNGkEWq3leFUVJD578p48D\niiNJySoD2ZB8/I/6TpZLNMYSxPzzt8EPLefz0sE+lCmdQO7Bn0g1LjUoihJQgjFXWFbgEeAgkir5\nLqAEzstX+ho1LEFIQrzBnDcXU+KHPpQpdI2CI/qTrVVLyKqDDhUlGAjG4H0NYC9iVDoBHyMxF8UF\nVvG7ekPnsYnL2VnwSSr/9BbFB/eg8LFtZGvTyqtGJTO1pz9Qnd7DChoDgSf//aOAa4gbrCdwAJjg\nS1FK8BO/YjVHytfn5ktdOf50N0pd3EnxHu20l6Ioikddoy2IK6wvUgP5R6TWcVUf6nKFusICzbp1\nXHunD5c27WdC6d60mf0i5Sp6MgZEUZRAEWy5wgCuAB8CHYE6SPD+Dl+KUoKQTZswevfhytod9Iv9\nmHIDI3jvzRzaQVEUJQWevBbaATeR+SwnkcD9l74UZXWs4nf1SOe2bdCyJbFNm/PV7qa0rbyfN7Z3\nI/Jt/xmVDNWeQYDq9B5W0BgIPHk1nAB+BkKAp4EYNMaS8dm1C9q2xWjcmCVx4ZSNP0C+D15nwbKc\nlC2b+uWKomRePPG5PYf0UFaY208C7wLTfCXKDRpj8TV790pN4MWLOdnxv7SNfp1cd93J6NEQFhZo\ncYqipIdgnMeyHXgKOG1uFwaWApV8JcoNalh8xcGD8OmnMG8e8d3fZkjsm3w5Kh+ffw7/+Y/OcVQU\nKxOM81iyAGfsts9hvazIfsUqftfo6GhJ6tW1K1SrBmFh7PjtAI/P/IhlG/KxaZMcCrRRsVR7WgDV\n6T2soDEQeGJYfgcWAhHAy8B8YIEPNSn+4MwZyg8bBlWrQpEi3Nq5j3704/9aFaB7d1iwAEqXTv02\niqJYkrHAKWCHk2PvAAlI0mEbvYD9wF9IQmK3ePJdNAvQCql7bwCrgN88uM4XqCvMG+zdC02bytKn\nD1uOFiYiAkqVgu+/hxIlAi1QURRv4sQVVge4igzEsi/ZWgoYDVRA8kGeBx4AJgGPI6OClwD3IcbH\nKZ7MYzGA6eaiWJ3Vq6F1a/j8c2527MKAAfDDD/DVV9CxY+DdXoqi+IVVQJiT/UOB94BZdvtaAJOB\nWOAQkn3lCWCdq5u7c4VdRSZHOlsueyg+UxK0ftdffoFnn4UJE9hbuwsVK0azYwds3QqdOgWvUQna\n9nRAdXoXK+i0gsY00AI4SsoEw8XN/TaOIj0Xl7jrseRNlzQl+DAMGDwYvvkGlizheOHKNKoJrVrB\nkCHBa1AURUkf0dHRaTV6eZAMKw3s9rl7M7iNSVjtlaIxlrQSFwfdu8Mff8C8eVzOX5Inn4R27aBX\nr0CLUxTFH7gYbhwGzEFiLA8jsZPr5rGSSG7IasigLUiqHPw7kjvyT1fP82VSDmejDj4FtgFbkbkw\npcz9YcANJOHlFmCkD3VlHq5ehRYtZI7KqlXEFi1JmzZQowZ88EGgxSmKEkTsAIoC95jLUSTR8Clg\nNtAeyGEeKw+sd3czXxqWn4DGDvsGI+n3qwAzEatn4wCSRfkRINKHunxOUPhdT5yQ2vJFi8K8eRj5\n8vOf/0CuXDBihLi/gkKnB6hO76I6vYcVNLpgMvAHMrrrCEm9Ehv2rqHdwFTz5wLk/ezWdeTLfOfO\nRh1csVvPC5z14fMzL7t2QbNmMmX+o48gSxb69JZRxsuWQXbNcq8omZ0OqRx3zAj4ubl4hCcxltaI\nb62o3fkGkN+Da8NI8uHZ+AypRHkdqI5UpgwDdiITcC4hVSpXO7mfxlhSY9kyaN8ehg6V8cPIcOIv\nv5QwS+HCAdanKIrfCcZcYQeRrMZ70nH/MFIaFhsfIJNwXkZ8d3cCFxC/3kzgQZL3cEANi3uiouCd\nd2RYcb16AMydK2lZVq2CcuUCrE9RlIAQjIW+TpI+o5Iak5D0MAC3zAWkOuVBJEC02fGiiIgIwsw0\nuyEhIVSpUoXw8HAgyd8Z6G3bPr89v25dGDCAmG+/ZfugQTxhGpXvvoumVy9YtCiccuVSXv/1118H\nZfsFvD3Tua3t6d1tK7Tn1q1befvtt4NGj7vtYKG1ufwP+AXxydn2tfLwHmEkHxVW3m69OxBlrhdC\nKlOC+PaOIvVfHDGswPLly/33sFu3DOPllw2jalXDOH48cfeBA4ZRrJhhzJ7t+lK/6rwNVKd3UZ3e\nwwoaDcMwSCXY7m3cdY3GkSQmCymFOY4icGQyUBcxGqeQEWBNEfdXPNIreQ1Jx98K+ARJGZAA9AHm\nObmn2UYKAJcvQ5s2kCMHTJkCeWVO65kzULMm/Pe/8MorAdaoKErACcYYS21SBtKd7fMHalhsHD0q\nSSRr1ZLxw+ZQr+vXoX59eOopGDAgwBoVRQkKgrEey3AP9ykm9r5sn7B1q8xy7NgRRo5MNCrx8dCh\nA1SoIDW7Aq7TS6hO76I6vYcVNAYCd8H7GkBNoAjQkyRrl4+keIjibxYuFIPy7bfw3HOJuw1DMrdc\nvw7Tpmn+L0VRAoe7109doB7wCjDKbv8VZAjxfh/qckXmdoX9+KNMeJw+HWrXTnZo4EAJs6xcCfk9\nmWGkKEqmIRhjLGFIDv5gIHMaFsOA3r1h8mSYP198XXZMnAgffywTIIsXD5BGRVGClmCMsVwHvkLm\nnCw3l2W+FGV1vOp3vXlTXF9LlsDatSmMytKlMidy/vy0GxWr+IdVp3dRnd7DChoDgSeG5WekznFZ\noB/Se9noO0lKIhcuQKNGcOOGpGopUiTZ4W3bJFg/bRo88ECANCqKojjgSddoM5JmZTtQydy3EXjM\nV6LckHlcYYcOyXDiRo2kbnC25OMljhyRuSpDhiSL4SuKoqQgGF1htlQrJ5GcYVWBUJ8pUmDjRrEa\nr7wCw4alMCoXL0KTJtCjhxoVRVGCD08MywAkvco7wH+BH4EevhRldW7L7zpnjliNkSPhrbdSHL55\nE1q2hAYNoGfP9D8GrOMfVp3eRXV6DytoDATu5rHkBl4FygElgDFAuB80ZV6+/Vamy8+dC9WqpTic\nkAAREZL6fsgQ/8tTFEXxBHc+t6mIG2wVkuPrEJDyK7R/yZgxloQEeP99mD0bFiyAso41doR334V1\n62DxYqkEqSiK4gnBlDb/fpLqqIwBNvheTibkxg148UU4eVImotx1l9PThg+XjsyaNWpUFEUJbtzF\nWOJcrCup4LHf9exZyRaZLZt0Q1wYlRkzYPBg6cwULBgAnQFGdXoX1ek9rKAxELgzLJWQ9C225WG7\n9cu+l5bBOXBARn7VqQOTJrnshqxZA6++KjF9s76ZoihKUGO1VIUZI8aydi20agX9+rktmPLXXxAe\nDhMmQMOGflOnKEoGI5hiLIovmD5duiDjx8sESBecPCmHBw5Uo6IoirXwZB6Lkkac+l0NQyY7vvWW\npL53Y1SuXIFmzeDll2V4sa+win9YdXoX1ek9rKAxEKhh8Qfx8WJQxoyRkV9Vq7o8NTZWZtM/+qhk\nLFYURfEBY5GS8Tvs9n0KbAO2AkuBUub+MOAGsMVcRqZ2c42x+Jpr1+D556UbMmMGhIS4PNUwoEsX\nOHUKZs1KLAypKIpyWziJsdQBrgITSJpWkg8ZnAXQHagM/AcxLHPszksV7bH4klOnoF49KFAAfv/d\nrVEB6N8fduyAX35Ro6Ioik9ZBVxw2HfFbj0vcDa9N1fD4gOio6NlSFeNGpL3a/x4yJHD7TVjxkBU\nlEyCzJvXjzotgOr0LqrTe1hBYxr5DPgXeAkYaLf/HsQNFg3UTnlZcvR7sQ8osG0btGsnQ7pefjnV\n8+fPl4rDK1dC0aJ+EKgoSoYmOjo6vUbvI3P5ABgGvAwcR+ItF5Ds9jOBB0new0mGxli8zeTJEqif\nNElm1afCxo3SqZk9Wzo4iqIo3sbFPJYwXMdOSiNVgx9ycmw5ku1+s6vnaY/FWxgGDBok6e6XLoWH\nU49z/f03NG8OP/6oRkVRlIBTHthvrrdAXF8AhZDeSjxSSbg88Le7G2mMxRvExcmkxylTYO1aos+d\nS/WSs2elp/LRR9CihR80OsEq/mHV6V1Up/ewgkYXTAb+ACoAR4DOwBfI8OOtSImUd8xzn0SGIW8B\npgGvABfd3Vx7LLfLlStJZRxXrYJ8+WD/freX3LghPZVnn4XXX/eDRkVRlOR0cLJvrItzZ5iLx2iM\n5XY4flymyD/2mLjA7rgj1Uvi46FtW8iTR3KAZdU+o6IoPiYYa94rzti5UwIjbdvCDz94ZFQMA95+\nGy5dgrFj1agoipIx0Vdbeli6FOrXhy++gA8/hCzJvwi48rt+9RWsWCET8FOZ1uIXrOIfVp3eRXV6\nDytoDAQaY0kr48fDe+/BtGlQt67Hl02eDCNGSKqwAgV8qE9RFCXAaIzF8ydLzpXx42VG4/33e3zp\n8uUyX3LZMnjI2ahwRVEUH6L1WIKRW7egWzfYtUuKdN19t8eX7tghRuWXX9SoKIqSOdAYS2pcuiS1\nU86fh+hoj4yKze969KgMGvvf/yQXZbBhFf+w6vQuqtN7WEFjIFDD4o5//4VataBiRfjtN7jzTo8v\ntdmj7t2hg7MR44qiKBkUjbG4YssWeOYZ6NEDevZMMfLLHbduQePG8OCDMHx4mi5VFEXxOv6OsVjt\nlecfw7JgAbz4Inz3HbRpk6ZLExLk0uvXZeBYtmw+0qgoiuIhGWmCZFpKXwL0QhKg/QU09KEu9/zw\ng6S6nzUrzUYFZFrLli3R/Pxz8BsVq/iHVad3UZ3ewwoaA4EvDctPQGOHfYORcpdVkJz+fc39DwDt\nzJ+NkZrK/o3/JCRAr17w5ZeS86tmzTTf4ttvJRTz+eeQO7cPNCqKolgAX3eNwnCd778XUAApKNML\nSAAGmcd+B/oB6xyu8Y0r7OZNiIiAw4elp1K4cJpvMXMmREbCmjVwzz3el6goipJeMsM8ls+ATsAN\n4AlzX3GSG5GjQAm/qDl/XtIMFy4sqVrS0dVYuxa6dpXQjBoVRVEyO4EwLPalL79GSl86w2nXJCIi\ngrCwMABCQkKoUqUK4eHhQJK/09PtdZMmUemDD8jz3HMweDDRK1em6fro6GiOHIF33w1nwgS4ejUa\nm8s1PDw8zXr8vf3111/fVvv5a9u2L1j0aHv6Z9sK7bl161befvvtoNHjbjsjEUby4L09pYGd5voH\n5mLjd6Cak2sMr/Hnn4ZRrJhhjBiR7lucPGkYZcsaxo8/Jt+/fPny29PmJ1Snd1Gd3sUKOq2g0TAM\nAxdf1H2Fv2Ms9qUvuyOusE5I0H6SuV0CWAKUI2VjmG10m8yaBf/5D4wZIxW30sHVqzKbvlkz6Nfv\n9iUpiqL4iowUY5kM1EXqJR9BRoA1RUphxgMHgdfMc3cDU82fcUAkvrKwI0ZIuvv58+Hxx9N1i7g4\nyf9VqRL07Zv6+YqiKJkJXw7p7YAE5XMg81XGAm2Q3ksVoDVw2u78z5FeSkVgodfVJCTIDPqRI2Xo\nVjqNimHI6K+EBBg1yvmsentfdjCjOr2L6vQuVtBpBY2BIHNkN75xAzp2hLNnpSBKaGi6bzVgAGza\nJAW7PCgaqSiKkunI+CldzpyBFi1kHPDYsZAzZ7ofPm4cfPKJ2KY0ZM5XFEUJKBkppUvg2b9fZtDX\nqwdRUbdlVBYuhA8+kLkqalQURVFck3ENy5o1UKeOlBH+7DPImv5fdfNm6NQJpk+HChVSP98qflfV\n6V1Up3exgk4raAwEGdOwTJsGLVuK76pr19u61aFDkj1/1CgpzaIoipIB8GmS4IwVYzEMGDIEvv4a\n5s6FKlVu62Hnz4sxiYyUgl2KoihWxEmMpQ5wFZhA0jzDfMAVc707kjD4PyTNM3ycpHmG9yH5HZ2S\ncUaFxcXBW2/BypWSvKtUqdSvcUNMjMydfPppNSqKomQ4ViET2O25YreeFzhrrrdA5iXGAoeAA8hk\ndsckwYlkDFfYtWuSSHLfPli9+raNSkKCjE4uVQoGDUr9fEes4ndVnd5FdXoXK+i0gsY08hnwLxAB\nfGHuK44kBraRapJg6/dYTp6UIMhDD0mRrtucXGIYMo/y7FkZCXYbMX9FUZSAEB0dnV6jd1tJgm1Y\nO8ayZw80bSoVH3v39kpx+aFDZbrL6tUQEnLbt1MURQk4LuaxhOG6XlZpYD7wEEkJggeaP39HUnT9\n6ep51v0+Hh0N4eHQvz/06eMVo/LLLzBsmMxVUaOiKEomo7zdegtgi7k+G2iPpOe6xzxvvbsbWdOw\n/PwzPPccTJoEL77olVuuWCFB+nnzbjtEYxm/q+r0LqrTu1hBpxU0umAy8AeSFPgI0BmJqexAhhuH\nA++Y59onCV6AB0mCrRdj+ewziaUsWyZxFS+wa5fYqcmTJWOxoihKBqeDk31j3Zz/ubl4hKViLD+A\n0fWRR2SOSvHiXrnn8eNQo4bYq44dvXJLRVGUoCIj1WPxOtdA5qnkzeuV+12+DE2awGuvqVFRFEXx\nFpaKsfQArxmVW7egdWuZWf/++165ZSJW8buqTu+iOr2LFXRaQWMgsJRh8RaGIZWJ8+SRgpJeGFCm\nKIqimFjtleqVmvcffQRLl0r8P08eL6hSFEUJYjTG4mNGjYKpU6VYlxoVRVEU75OpXGFz5kgFyN9/\nh8KFffccq/hdVad3UZ3exQo6raAxEGSaHsuff0LnzjB/Ptx7b6DVKIqiZFwyRYzlwAEpJjl6tKTB\nVxRFyUxozXsvc+aMzFXp31+NiqIoij/I0Ibl2jUxJu3bQ7du/nuuVfyuqtO7qE7vYgWdVtAYCDKs\nYYmLE4NSsaIE7BVFURT/kCFjLIYhaVr+/lvSiuXI4QdliqIoQYrOY/ECX3who8BWrFCjoiiK4m8y\nnCtswgTJqj9vHuTPHxgNVvG7qk7vojq9ixV0WkFjIMhQPZbFi+Hdd2H5cq9l1VcURVHSSIaJsWzd\nCg0bwvTpMmdFURRFEXQeSzr4918ZVvztt2pUFEVRAo3lDcuFCzIB8p13oG3bQKsRrOJ3VZ3eRXV6\nFyvotILGQGBpwxITAy1bQqNG0KNHoNUoiqIoYOEYS0ICdOggc1amTIGsljaRiqIovkPnsXjIu+/C\niROwaJEaFUVRlGDCl6/kscApYIfdvi+BPcA2YAZQwNwfBtwAtpjLSHc3/vprWLAAZs6EXLm8rNoL\nWMXvqjq9i+r0LlbQaQWNLvDZ+xl8a1h+Aho77FsEPAhUBvYBveyOHQAeMZdIVzf99Vf46isxLAUL\nelewt9i6dWugJXiE6vQuqtO7WEGnFTS6wCfvZxu+NCyrgAsO+xYDCeb6n0DJtN40MlLyf5Upc5vq\nfMjFixcDLcEjVKd3UZ3exQo6raDRBT55P9sIZHSiMzDfbvsepJsVDdR2ddHEiVClim+FKYqiZHLS\n9X62Eajg/UfALWCSuX0cKIVY0KrATKRLdsXxwoYN/aTwNjh06FCgJXiE6vQuqtO7WEGnFTSmg3S/\nn/1FGMmDQwARwBrAXdh9OfILOHIAMHTRRRdddEnTcoCUhOHd97PfCCO58MbALqCQw3mFgGzmelng\nKBDia3GKoiiZmDAs+H6ejHShbgFHEJ/dfuAwKYettQZ2mvs2Ac38LVZRFCUToe9nRVEURbkdnE3c\ncWQ4Yl23IeOqA0FqOsOBSyRZ/4/9IysFpRCf6C7kW8ebLs4LdJt6ojOcwLdpLmQo5lZgN/CFi/MC\n3Z6e6Awn8O0J4mbZAsxxcTzQbWnDnc5wgqMtDwHbTQ3rXZwTLO3pV+ogv6yrF3ZTkobBVQPW+UOU\nE1LTGQ7M9psa19wN2AZo5wX2Avc7nBMMbeqJznCCo03zmD+zI23lOPwyGNoTUtcZTnC0Z0/gZ5xr\nCZa2BPc6w13s9zf/AO6mjvulPYMxy5aziTv2NAfGm+t/IkGkor4W5YTUdEJwJPk8iXxrBbiKpGxw\nrK8ZDG3qiU4Ijja9bv7MgXyLPe9wPBjaE1LXCYFvz5LIy+5HF1qCpS1T04mb/f7GnQ6/tGcwGpbU\nKIEEm2wc5TZmiPoQA6iJdDfnAw8EVg4go0AeQT5Q9gRbm4bhXGewtGlWxAieQtx3ux2OB0t7pqYz\nGNpzGPAuSTO+HQmWtkxNZzC0pU3HEmAj0NXJcb+0pxUNC6S0yEZAVLhnMxI3qAyMQCYVBZK8wK/A\nW0iPwJFgaVN3OoOlTRMQt11J4EnEDeJIMLRnajoD3Z5PA6eReIC7b9mBbktPdAa6LW3UQr6UNQFe\nR1z2jvi8Pa1oWI4hf0AbJc19wcYVklwRC4A7cO/79CV3ANOBiTj/wAdLm6amM5jaFCRYOw94zGF/\nsLSnDVc6A92eNRHXzD/I8Nf6wASHc4KhLT3RGei2tHHC/HkG+A14wuF4MLRnwAjDs+B9dQIbzAvD\ntc6iJH0zeAIZrREIsiD/BMPcnBMMbeqJzmBo00IkTQ7LDawE/s/hnGBoT090BkN72qiL89FWwdCW\n9rjSGQxtmQfIZ67ficygd0yC5Zf2DMZCX5ORP14hxBfYF7H+AN8jjdIUSVFwDXg5ABohdZ1tgNeA\nOOSbTPsAaATpGnckaQgiwIdAaXM9WNrUE53B0KbFkOBnVnOJApYCr5jHg6U9PdEZDO1pj80lE2xt\n6YgzncHQlkWRXgrIu/1nJBV+sLenoiiKoiiKoiiKoiiKoiiKoiiKoiiKoiiKoiiKoiiKoiiKkvmI\nR+aP7ETyS/UkacLXo8D/3FxbBujgU3XueRPJgxXl4+e8jUw2DAaeAd4311uSPBP0S8g8lvTwBlKe\nFqA/KSdVuiMCSWeSXpaSNMFPUZQMwBW79cLAYqCfh9eG47qWhj9wlQnZ2xOA/wHucnEskOmRxiFV\n/mwsR74MpIVsyBeJLaS/3V7i9gxLV+QLjaIoGYQrDtv3AGfN9XCSDEddkooZbUKSRq4DLpr73kJ6\nMCvN45uAGnb3iQamIcZgot3zHkdSUGxFshrfibzsvkQKFm0DujnRPQq4iczYfxvJgBAFrEZmHpcB\nlpnXLyEpT9I4pPTqWuCgqW080vP5yclz3rR7zlJz31XgK1NzLaC3qXUHMsvZRjQw0Py99pJUF+VB\nc98WU9+9SJqgv0wNe83foaHZNvvMdoKk3kEN4Bzwt3mf95C/5V9IYsRciJGJRjLe/o7UvrHpGgZs\nQF7otZGMEjbGkWSwDiFfNDaZbVDBSRvZG5ZmwB+IIR6HZ21dFNfFqRRFsSCOhgWk3kxhkhuW2SQZ\nijzIy98xf1JuIKe5Xh55cWHe5yLSu8iCvHhqIvVCDpL0LTuved9uwEfmvpzmfcKc6LQvbtTPPM/2\n/DlAJ3P9ZZJSXowDJpnrzYHLyIs+C/ICrpzKc0CyB7ex2w61W5+AZMUF6UF8aa43QXqDIC/h5831\n7LNdT4kAAAMiSURBVIgRCANiHbSMsdNp0x9B0kv8J6CV3bOXA1XN9TtIesEDtLO733LgG7vrPiB5\nj8H+vv8g2XJBUpeMJiU2w/Is8sWigN19PG3rv5EvFYqFCcZcYUpwswb5lvszMAPJjOqYhjsH8sKq\njMRuytsdWw8cN9e3Ij2jK0hW1k3mflu6/IbAwyS9vPMD5XCf4M9AjN9Nc7s6EoMA6SENtjvPZgx3\nIoXGdpnbu5AX/DY3zwH53abbbddHanbkQQzQTmCueWyG+XMzScbxD8RwljSPHzD3/+OgZYmdTtu1\njjj+DWzbFZCXuO0e2Uhqf4Bf7NZLIz09V9j/Dq2cHM+CtMFjQAOSlz3wtK1PIb3Kv9zoUIIcNSyK\nK8oiL84zDvsHIS/LZoiRaeTk2h6IoeiEvMhi7I7dtFuPRz6D7upBvEHSN3xPue6w7aqGxi3zZ4KD\nrgQ8+9+IIUl7LuBbpNd1DHHJ5bI713Z/2+8M4nZah/Rs5iPJAv9xosVepytdjm1o286CvLxrurju\nmsO2u7oozn4Hx2ceRL4sVCDpiwJ43tZZCM76SkoasGI9FsX3FEZiF84CsfciL6rBiMupAuLasB/N\nkx/5VgrwImJcXGEgsYRiJNULyWdesxCIJOnFcx9Jddw95Q+SMs2+gLho0ssV5Hdzhs2InENceW09\nuF9ZxJCMAGYhvbP0vFQdddlv70X+ntXN7TtwXd3wMEnxl/SQxbxHG8QVmJ4qikWRqoaKhVHDotjI\nTdJw48VIkLe/ecwg6YX3FhKc3oZ8C12ABHPjEdfWW0ig9iVzuwLJXSLOXpyxiO9/hHnNQiRG8iMS\n4N1sPvM7XH9TdrXdHYmtbEMMy1suznN3Dxs/IO2y1Mk5F5G4w07zHMeyys7u/Zx5/hbEXTUB59/Y\nnem0/5tMQVxwmxBjNQ75YrAZ+R9vg/Q0t5rPqoFzVpOyGJgr/c7ax7Z/L9LW00w9rn4Hx+27EcPs\n2ItSFEVRLIptuHGOAD2/G+JGVRRFUTIQkQSu+NNSxI2oKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqi\nKIqiKIqiKIqiZEz+H0C1ZRMhMDC/AAAAAElFTkSuQmCC\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0xb55925ac>"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "L50 values by Cost 231 model\n",
+        "129.03  140.47  147.16  151.91  155.59 \n",
+        " \n",
+        "L50 values bu Okumura/Hata model\n",
+        "125.13  135.73  141.93  146.34  149.75 \n",
+        " \n",
+        "The results from the plot of two models shows that the calculated path loss with the COST 231 model is higher than the value obtained by the Okumura/Hata model.\n"
+       ]
+      }
+     ],
+     "prompt_number": 3
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Example 3.8, Page 76"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import math\n",
+      "\n",
+      "#Variable declaration\n",
+      "SNRmin=12;#in dB\n",
+      "n=3; #No of floors\n",
+      "Backgroundnoise=-115; #dBm\n",
+      "pt=100  #in dBm\n",
+      "\n",
+      "#Calculations\n",
+      "pt_db=10*math.log10(pt);\n",
+      "Sr=Backgroundnoise+SNRmin; #receiver sensitivity\n",
+      "Lpmax=pt_db-Sr;\n",
+      "#Refering table 3.4\n",
+      "Lp_d0=38;  #ref path loss at the first meter(dB)\n",
+      "Lf=15+4*(n-1); #signal attenuation through n floors\n",
+      "y=3; #path loss exponent\n",
+      "X=10; #Shadowing effect(dB)\n",
+      "d=10**((Lpmax-Lp_d0-Lf-X)/30); #max allowable path loss\n",
+      "\n",
+      "#Result\n",
+      "print 'Coverage radius of an access point = %d m \\n'%(round(d))"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Coverage radius of an access point = 54 m \n",
+        "\n"
+       ]
+      }
+     ],
+     "prompt_number": 22
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Example 3.9, Page 77"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import math\n",
+      "\n",
+      "#Variable declaration\n",
+      "SSmean=-100; #signal strength(dBm)\n",
+      "Sr=-110; #receiver sensitivity(dBm)\n",
+      "sd=10;  #standard deviation(dB)\n",
+      "\n",
+      "#Calculations\n",
+      "P_Smin=(0.5-0.5*math.erf((Sr-SSmean)/(math.sqrt(2)*sd)));\n",
+      "\n",
+      "#Result\n",
+      "print 'probability of exceeding signal beyond the receiver sensitivity is %.2f \\n'%(P_Smin)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "probability of exceeding signal beyond the receiver sensitivity is 0.84 \n",
+        "\n"
+       ]
+      }
+     ],
+     "prompt_number": 24
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Example 3.10, Page 81"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import math\n",
+      "\n",
+      "#Variable declaration\n",
+      "Lp=140; # path losses in dB \n",
+      "k=1.38*10**-23; # Boltzmann\u2019s constant (W/Kelvin-Hz)\n",
+      "k_db=10*math.log10(k);\n",
+      "f=900;#in MHz\n",
+      "Gt=8; #transmitting antenna gain(dB)\n",
+      "Gr=0; #receiver antenna gain(dB)\n",
+      "Ag=24;#gain of receiver ampli\ufb01er in dB \n",
+      "Fmargin=8;#Fade margin(dB)\n",
+      "Nf=6;#Noise figure(dB)\n",
+      "L0=20; #\u0002 other losses in dB\n",
+      "Lf=12; # antenna feed line loss in dB \n",
+      "T=24.6;#Temperature expressed in dB\n",
+      "R=39.8; #\u0002 data rate in dB \n",
+      "M=8;  #overall link margin(dB)\n",
+      "Eb_No=10;#dB\n",
+      "\n",
+      "#Calculations\n",
+      "#From equation  (3.54)\n",
+      "pt_db=M-Gt-Gr-Ag+ Nf + T+ k_db+ Lp+ Lf+ L0 + Fmargin+ R+ Eb_No;\n",
+      "\n",
+      "Pt=10**(pt_db/10);  #dB into normal number\n",
+      "\n",
+      "#Result\n",
+      "print 'Total transmitted power is %d Watts \\n'%Pt"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Total transmitted power is 6 Watts \n",
+        "\n"
+       ]
+      }
+     ],
+     "prompt_number": 25
+    }
+   ],
+   "metadata": {}
+  }
+ ]
+}
\ No newline at end of file