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+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Chapter 6 Baseband Shaping For Data Transmission"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Example 6.1(a) page 235"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x7fc3cc0e5790>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "from __future__ import division\n",
+ "%matplotlib inline\n",
+ "from matplotlib.pyplot import plot,grid,title,show\n",
+ "\n",
+ "#Figure 6.1(c):Discrete PAM Signals Generation\n",
+ "# [3].BiPolar NRZ\n",
+ "#page 235\n",
+ "x = [0, 1, 1, 0, 0, 1 ,0 ,0 ,1 ,1]\n",
+ "binary_negative = [-1, -1 ,-1 ,-1 ,-1 ,-1 ,-1 ,-1 ,-1, -1]\n",
+ "binary_zero = [0 ,0 ,0 ,0 ,0, 0 ,0 ,0 ,0 ,0]\n",
+ "binary_positive = [1, 1 ,1 ,1 ,1 ,1 ,1 ,1 ,1 ,1]\n",
+ "L = len(x)\n",
+ "L1 = len(binary_negative)\n",
+ "total_duration = L*L1\n",
+ "#plotting\n",
+ "for i in range(0,L):\n",
+ " if(x[i]==0):\n",
+ " plot(range(i*L-L,i*L),binary_zero)\n",
+ " \n",
+ " elif((x[i]==1) and (x[i-1]!=1)):\n",
+ " plot(range(i*L-L,i*L),binary_positive)\n",
+ " \n",
+ " else:\n",
+ " plot(range(i*L-L,i*L),binary_negative)\n",
+ " \n",
+ "grid()\n",
+ "title('BiPolar NRZ')\n",
+ "show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Example6.2 Page 241"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Precoder output in binary form:\n",
+ "1 \t1 \t0 \t0 \t1 \t0 \t0 \t\n",
+ "\n",
+ "Precoder output in volts:\n",
+ "1 \t1 \t-1 \t-1 \t1 \t-1 \t-1 \t\n",
+ "\n",
+ "Duobinary coder output in volts:\n",
+ "2 \t2 \t0 \t-2 \t0 \t0 \t-2 \t\n",
+ "\n",
+ "Recovered original sequence at detector oupupt:\n",
+ "0 \t0 \t1 \t0 \t1 \t1 \t0 \t"
+ ]
+ }
+ ],
+ "source": [
+ "from __future__ import division\n",
+ "\n",
+ "b = [0,0,1,0,1,1,0]##input binary sequence:precoder input\n",
+ "a = [1^b[0]]\n",
+ "if(a[0]==1):\n",
+ " a_volts=[1]\n",
+ "\n",
+ "for k in range(1,len(b)):\n",
+ " a.append(a[(k-1)]^b[(k)])\n",
+ " if(a[(k)]==1):\n",
+ " a_volts.append(1)\n",
+ " else:\n",
+ " a_volts.append(-1)\n",
+ " \n",
+ "print 'Precoder output in binary form:'\n",
+ "for aa in a:\n",
+ " print aa,'\\t', \n",
+ "print '\\n'\n",
+ "print 'Precoder output in volts:'\n",
+ "for bb in a_volts:\n",
+ " print bb,'\\t',\n",
+ "print '\\n'\n",
+ "#Duobinary coder output in volts\n",
+ "c= [1+ a_volts[0]]\n",
+ "for k in range(1,len(a)):\n",
+ " c.append(a_volts[(k-1)]+a_volts[(k)])\n",
+ "print 'Duobinary coder output in volts:'\n",
+ "for cc in c:\n",
+ " print cc,'\\t',\n",
+ "print '\\n' \n",
+ "#Duobinary decoder output by applying decision rule\n",
+ "b_r=[]\n",
+ "for k in range(0,len(c)):\n",
+ " if(abs(c[(k)])>1):\n",
+ " b_r.append(0)\n",
+ " else:\n",
+ " b_r.append(1)\n",
+ "print 'Recovered original sequence at detector oupupt:'\n",
+ "for brr in b_r:\n",
+ " print brr,'\\t',"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Example6.3 page 246 "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Modulo-2 adder output:\n",
+ "1 \t1 \t0 \t1 \t1 \t0 \t0 \t0 \t0 \t1 \t0 \t\n",
+ "Delay element output:\n",
+ "1 \t1 \t0 \t1 \t1 \t0 \t0 \t0 \t0 \t1 \t\n",
+ "differential encoder bipolar output in volts:\n",
+ "1 \t1 \t0 \t1 \t1 \t0 \t0 \t0 \t0 \t1 \t"
+ ]
+ }
+ ],
+ "source": [
+ "from __future__ import division\n",
+ "#for generating bipolar format\n",
+ "#Refer Table 6.4\n",
+ "\n",
+ "x = [0,1,1,0,1,0,0,0,1,1]##input binary sequence:precoder input\n",
+ "y= [1]\n",
+ "for k in range(1,len(x)+1):\n",
+ " y.append(x[(k-1)]^y[(k-1)])\n",
+ "\n",
+ "y_delay = y[0:-1]\n",
+ "print 'Modulo-2 adder output:'\n",
+ "for yy in y:\n",
+ " print yy,'\\t',\n",
+ "print '' \n",
+ "print 'Delay element output:'\n",
+ "for yyy in y_delay:\n",
+ " print yyy,'\\t',\n",
+ "print '' \n",
+ "z=[]\n",
+ "for k in range(0,len(y_delay)):\n",
+ " z.append(y_delay[k])\n",
+ "print 'differential encoder bipolar output in volts:'\n",
+ "for zz in z:\n",
+ " print zz,'\\t',"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Example 6.4 Page 247"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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6dC3s3QtriBVpnihrOY0bw7Rp8NRTMGYMtG0L8+bBTTeVNWeNRqPRFIbZLiyb4u8PX38N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\nLJaQ+D+L1IOnq28nhgAAAABJRU5ErkJggg==\n",
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x7f2eb08bd5d0>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "from __future__ import division\n",
+ "from numpy import arange,sinc,sin,pi\n",
+ "%matplotlib inline\n",
+ "from matplotlib.pyplot import plot,grid,title,show,xlabel,ylabel\n",
+ "\n",
+ "\n",
+ "\n",
+ "#Different Line Coding Techniques\n",
+ "#[1].NRZ Polar Format [2].NRZ Bipolar format\n",
+ "#[3].NRZ Unipolar format [4]. Manchester format\n",
+ "\n",
+ "#[1]. NRZ Polar format\n",
+ "a = 1 # The Amplitude value\n",
+ "fb = 1 # The bit rate\n",
+ "Tb = 1/fb# #bit duration\n",
+ "f = arange(0,1/(100*Tb)+2/Tb,1/(100*Tb))\n",
+ "Sxxf_NRZ_P=[]\n",
+ "Sxxf_NRZ_BP=[]\n",
+ "Sxxf_NRZ_UP=[]\n",
+ "Sxxf_Manch=[]\n",
+ "for i in range(0,len(f)):\n",
+ " Sxxf_NRZ_P.append((a**2)*Tb*(sinc(f[i]*Tb)**2))\n",
+ " Sxxf_NRZ_BP.append((a**2)*Tb*((sinc(f[i]*Tb))**2)*((sin(pi*f[i]*Tb))**2))\n",
+ " if (i==0):\n",
+ " Sxxf_NRZ_UP.append((a**2)*(Tb/4)*((sinc(f[i]*Tb))**2)+(a**2)/4)\n",
+ " else:\n",
+ " Sxxf_NRZ_UP.append((a**2)*(Tb/4)*((sinc(f[i]*Tb))**2))\n",
+ " \n",
+ " Sxxf_Manch.append((a**2)*Tb*(sinc(f[i]*Tb/2)**2)*(sin(pi*f[i]*Tb/2)**2))\n",
+ "\n",
+ " \n",
+ "\n",
+ "#Plotting\n",
+ "plot(f,Sxxf_NRZ_P)\n",
+ "plot(f,Sxxf_NRZ_BP)\n",
+ "plot(f,Sxxf_NRZ_UP)\n",
+ "plot(f,Sxxf_Manch)\n",
+ "xlabel('f*Tb------->')\n",
+ "ylabel('Sxx(f)------->')\n",
+ "title('Power Spectral Densities of Different Line Codinig Techniques')\n",
+ "grid()\n",
+ "show()\n",
+ "#Result\n",
+ "#Enter the Amplitude value:1\n",
+ "#Enter the bit rate:1 "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Example 6.6 page 249"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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HA6OrPPZwpf9/Axyd77hMYVm1Cp56Cl5/3XcktWvZ0i2l+sIL4RbYjUnHpuUwBW/0aLfI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\nXII4JV2CMMk2dSo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+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x7fc324486a10>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "from __future__ import division\n",
+ "from numpy import arange,sinc,sin,pi\n",
+ "%matplotlib inline\n",
+ "from matplotlib.pyplot import plot,grid,title,show,xlabel,ylabel\n",
+ "\n",
+ "\n",
+ "#Figure 6.6(b): Ideal Solution for Intersymbol Interference\n",
+ "rb = 1 # The bit rate\n",
+ "Bo = rb/2#\n",
+ "t =arange(-3,1/100+3,1/100)\n",
+ "x = sinc(2*Bo*t)\n",
+ "plot(t,x)\n",
+ "xlabel('t------>')#\n",
+ "ylabel('p(t)------->')#\n",
+ "title('SINC Pulse for zero ISI')\n",
+ "grid()\n",
+ "show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Example 6.7 page 250"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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b5m3MQVdLwkcE87OzNAyskbA0SLKzYcUKyGwzjyk9p5iDR47AqVN2fITF4oY1\nEvWAYPZrB0q3RYtg2DBYcrBMPGLUKJ8uVRrMz87SMLBGwtIgmTMHxlxxmAOZB7g41pnpddkyn7qa\nGjJlV3izy5fWX6yRqAcEs187ELoVF8OXX0J43wVM6DGBxiHOTK8+jkdA/Xx2dvnSwCxfevToUaZN\nm0ZsbCwhISGkpqYGRI6yWCNhaXB88w20bAkbTs9ncg/H1ZSebsZIDB4cWOHqACLC3LlzycrKYtOm\nTWzevPmcRXZWr17NpEmTuOaaazhy5Ah79+5l4MCBjBw5kr1799Yo76NHj5KXl1eyQh2YWVf79u3r\nlbzuiwUFgsLCwmrfGxISwtSpU/n00099KFHNsUaiHhDMfu1A6LZgAUycXMiiHxaVxiNWroThw8HD\nymfVpb4/O7t8ae0tX9q2bVvuuusuhg4dWu00/IE1EpYGx/z50PXSr+nSogsdIp0V1lxBawtgly8N\nxPKldRbXQhz1eTNqBC9LliwJtAh+o7Z1y8hQjYhQ/e3CmfrgwgdLT4werbpwoc/z86Rfpe+smRyk\n5ls16Nq1q0ZERGhkZKSKiF599dVaVFSkqqoHDhxQEdEdO3acd9+8efM0NDS0RO9OnTqVnIuLi9Ok\npCRVVe3Ro4fOmzev5NyCBQs0Li5OVVX37t2rIlKSX9l7K5I3Ojpao6Oj9Zprrjnvmm+++UZbtmyp\nqqqHDx/WkJAQzcjIOO+6snJXJu+SJUs0LCxMz54961G+qlJQUKAiovv37/dZmp7eN+d4heWrbUlY\nGg7HjpG8qIgRI2DZwSTGdXNmei0ogA0bTJ/YuoKvzEQ1cC0Hevr0aVJSUkhOTmb9+vUA5yxfWpZA\nL1+anp5Oeno6s2bNIjc3lxkzZhAXF0eLFi0YM2YMmZmZdXr50rqKNRL1gPru166IWtWtfXsKXnyF\nsZNy2HhkI6O6OO6lzZshLg58vIA81P9nZ5cvrb3lS+sq1khYGhSHvs+g1eAVDOkwhOZhzrKQq1eD\n26LylnOxy5fWzvKlAHl5eeTl5Z33fyCxRqIeUB/72ntLbeuWp+HsLkomsVti6cE1a0zPJj8QDM/O\nLl9aO8uXAjRr1oyoqChEhD59+pS7vnVtE9DlS0VkMvAXoBHwT1X9UznXJAAvAqHASVVNKOcaDaQe\n/iYlJaXeuy08Uau6ifDhJS/w3E/f48VJL3JZV2e21549YfZsqKAvfnXxpJ9dvtRSm9Rk+dKAGQkR\naQTsAMZB0j30AAAgAElEQVQDh4B1wE2qus3tmmhgJTBJVQ+KSGtVPVlOWkFtJCw+QoQ1d/yZifF/\n5ORDJwlrFAbHj0OvXpCW5tM5myoXxRoJS+1RX9e4HgbsVtV9qloAfABcVeaanwCfqupBgPIMhMXi\nDWfOmL8au49LO19qDATA2rVwySW1aiAslvpEIL+MWOCA2/5B55g78UCMiCwRkfUi4tvoVj0hGPza\nnqgt3ZYvN393ntl1bjzCz0HrYH52loZB4wDm7U1bOxQYAowDmgGrRWSNqu4qe+H06dOJi4sDzJD+\nQYMGlfiCXR9qfd3/9ttv65Q89XH/n/+EicCm9K30PnJdaaxg7VpSJk4Et9hBbclnsdQ2KSkpvPnm\nmwAl5WVlBDImMRyYqaqTnf1HgWL34LWIPAw0VdWZzv4/gfmq+kmZtGxMwlIhF/ZTtmwN4ZZbm/PW\nm5k0CmlkBpvFxMCOHdC2ba3KY2MSltqkvsYk1gPxIhInImHAjUDZyeK/AEaJSCMRaQZcAmytZTkt\n9ZwjR+DkobMAXNimnzEQAHv3QkRErRsIi6U+ETAjoaqFwD3AAkzB/6GqbhORGSIyw7lmOzAf+A5Y\nC/xDVRuckQhm90Rt6JacDONHmsj1gLb9S09s2AAXXeTXvCvSz7XGgt3s5u+tJgQyJoGqzgPmlTn2\nRpn9/wf8v9qUyxJcJCVB4qV58BX0b3Nh6YmNG2HIkIDI5BNXU04OtGkDGRngmjtIhD3jL2bIuq/Z\nudP/jaRgHsMDwa+fN9h+f/WAYH5J/a2bqjES8f12AtApwm2OoFpoSfhVv+bNoUcP+O67cw5nZB4h\nIQEWL/Zf1i6C+d2E4NfPG6yRsAQ1e/aYSV6PFpq5eUoa3qoBbUn4jMGDYdOmcw5lnj7BuHFaK0bC\nEvxYI1EPsDGJ6pOUBOPGwca9zkybxcXmb2qqcdF06ODX/P3+7AYMOK8lEV4IPYbtJCmp2rOFe00w\nv5sQ/Pp5gzUSlqAmKQkSE5XN+782B1ylZjC0IqBcI9G2cRT7QpIpKDAtKYulJlgjUQ8IZr+oP3Ur\nLjY9mzoP2UoLDTcHXUZiyxZTwPoZvz+7/v3NehhuTYaWIc1J3pvEuHHGSPqTYH43Ifj18wZrJCxB\ny+bNEB0NW/OSuKSVYxBc7qbt26FPn8AJ5yvatwcRMxjEIUrDWLJvCWMTi/1uJCzBjzUS9YBg9ov6\nUzdXPCJ5bzLDwrqbg64ady0ZCb8/OxHTItq8ueRQaMZp2jRrQ8fB35GcXGoX/UEwv5sQ/Pp5gzUS\nlqAlKQkSEgtZun8p/WhjDhYXm23HDujdO7AC+op+/eD770v3T5wgsVsi3+cm0bLlOfbDYqky1kjU\nA4LZL+ov3QoKYMUKaNXvG2IjY4nMyDUnVOHgQYiK8sua1mWplWfXqxfscua8jIyEoiISuyaQvC/Z\n73GJYH43Ifj18wZrJCxBybp10L07bEhPMlODHz9uXDOqxtV0wQWBFtF3xMeXGolGjUCExKhBrEhd\nweixBTYuYakR1kjUA4LZL+ov3dzjEeO6jTNGol0742qqxaB1rTy7+HjYaUaUU1wM7doRk11I95bd\naXHBOlasMC0rfxDM7yYEv37eYI2EJShJSoLRY8+y+uBqxsSNMUaifXvTktizxzQzgoUuXeDoUWMJ\nVI2ex46RGJfIhrQkuneHr78OtJCW+oo1EvWAYPaL+kO33FxYvx7Cuq/hgtYXEN0k2sQhYmNNTfvg\nQejc2ef5lketPLvGjc1MfkeOGP06d4bUVMZ1H+f3uEQwv5sQ/Pp5gzUSlqBjxQoYNAhWH0028Yjs\nbLN16FAauO7UKdBi+pZOnYxeqiWB7Mu6XMa6Q+sYNfYMycmBFtBSX7FGoh4QzH5Rf+jmikck7U0y\n8Yi9e6FbNxPUdRmJWmpJ1Nqz69y51Ej07g27dhEZHsmAdgNoHLeK9etNC8vXBPO7CcGvnzdYI2EJ\nOpKSYMSYbL49+i0ju4w0MYhu3UzvpoKC0vhEMOFqSRQXlxgJgHHdxrH6aDKDB5sWlsVSVayRqAcE\ns1/U17qlpZmOPvkdlnNRx4toFtoMdu826y6IwMmTZnxEaKhP8/VErT27du3g2DHTknB1iVUlsVsi\nSXuTSEz0T1wimN9NCH79vMEaCUtQsXQpXHopLD/gdH0F2LbNjIsICYH0dDOhU7DRogVkZhoj0aoV\nhIfDsWOM6DyCLce3cMmYTDtewlItrJGoBwSzX9TXurnHIxK7JZqDW7dC376mJZGRUSsjrV3U2rNz\nGYniYqOnE7xu0rgJl3S6hLPtlrNzp2lp+ZJgfjch+PXzBmskLEFFUhJcdNkpdqftZljsMFOzLtuS\nqEUjUWu4tyRCQs4ZYDeu2ziWH0zm0kvBlnmWquK1kRCR+0WktT+FsZRPMPtFfanboUMmJp0WuZSR\nXUYS1igM9u+HZs2gTRtTw65lI1Frz65FC9NKcrUk+vcvWYzIFZfwx3iJYH43Ifj18wavjISIDACe\nBe7wrzgWS/VJToaEBFiyL6k0HrFhQ+kKdC53UzDGJKKjTUsCjJ6DB8M33wAwtONQ9mXsY8ioEzYu\nYaky3rYkfgY8DNzmR1ksHghmv6gvdSuZr2lfcmk8YuNGuOgi838A3E21GpPIyDD/u4zEpk1QXEzj\nkMZc1uUyTkamcOKEaXH5imB+NyH49fOGSo2EiDQBpgKvAz+IyEhfZS4ik0Vku4jsEpGHK7juYhEp\nFJFrfZW3JbhQNUbiwhGHOJ5znIHtBpoT5bUkgjUmkZFhdASIiTGtC2eR63HdxpGyL5mxY7Gjry1V\nwpuWxLXAfFU9C/wb06qoMSLSCHgFmAz0BW4SkfPmb3au+xMwHxBf5F3fCGa/qK90c4YFsD9kCQlx\nCTQKcUZXl21J5OYGZ0wiIsLo5rbWtbvLyV/jJYL53YTg188bvDESd2KMA8BXwGgRifBB3sOA3aq6\nT1ULgA+Aq8q57l7gE+CED/K0BCmlrqYkEuMcV9PBg6Zm3bGj2XfVsiMjAyOkPwkJgSZNzj02ZIgx\nkkD/dv1JO5NG3+EHSEo615ZYLBVRoZEQkZbAYVXdCKCqhcDfgEt8kHcscMBt/6BzzD3/WIzheM05\n1CBf7WD2i/pKt+RkSExUs35Ed7eg9UUXlRoH199aGm0NtfzsGjU6d3/YMFi7FoAQCWFst7HsD1mC\naukaRTUlmN9NCH79vKFxRSdVNR24tcyxF3yUtzcF/l+AR1RVRUSowN00ffp04uLiAIiOjmbQoEEl\nTUXXg66v+99++22dkqeu7Scnp7BwIdz3ZCcK5hVwZPMRjspREjZuhCFDSq8PMXWilB07ICWlzsjv\ns33HCJbsX3IJrF9PSlISNGrEuG7jWLIvmX79uvDaa/Dii3VMfrvv9/2UlBTefPNNgJLyslJU1esN\n+HtVrq8kreGYWIdr/1Hg4TLX7AH2OlsWcAyYVk5aamm4bNyo2quX6uvrXtdbZ91aemLyZNXPPivd\nf+wxVVB9993aF7I2iIgw+rnTp4/qpk2qqrrj5A7t9EInffPNYr3uugDIZ6lzOGVnhWV1VUdcX1zF\n6ytiPRAvInEiEgbcCMx2v0BVu6tqN1XtholL3K2qs8tJy9KAccUjFu9dXDo+orgY1qyBESNKL3Ra\nEiV/gw0pp6E9fLj5HYD4mHhUle4X72bJEvMTWSyVUdWv5bivMlYT37gHWABsBT5U1W0iMkNEZvgq\nn2DA1VwMRnyhW1ISjE0sZsneJaXxiK1boXVrMzuqC1chWotGolafXSVGQkQY130cW3OTadMGHC9m\njQjmdxOCXz9vqOrXMt2XmavqPFXtrao9VfUZ59gbqvpGOdfeoaqzfJm/pf6Tnw8rV0Kb/pto1awV\nnaKcFedWrTLTwbrjKkTLBniDhUqMBEBiXGLJkqZ2vITFG6pqJL70ixSWCnEFoIKRmuq2dq2Zy279\nqSTGdxtfeqI8IxEAd1OtPrvyjES/fqYrsDMae2y3sSTvTWZsYrFPxksE87sJwa+fN1T1a2mQg9ks\ndZdzlip1uZqg4pZEsMYkyqNxYxg61PweQJcWXYhuEk27/ltYudK0xCyWiqjq1/IPv0hhqZBg9ovW\nVLekJBg9Np+VqStJiEswB0+cMKu09e177sUBaEkEPCYBMHo0LF9esjuu2zg2pCXTq1fJMIpqE8zv\nJgS/ft5Q1a+lyC9SWCzVIDvbzDoR2m0NvVr1IqZpjDmxapXxxZeNPTTElgQYI7FsWcluYrdEkvcm\n+21JU0twUdWv5S6/SGGpkGD2i9ZEt2XLHE/KkSTGd3eLRyxdCmPGnH9DAIxErT47T3oNH25mhM3N\nNTLFJbBs/zISEgtrbCSC+d2E4NfPG2xMwlJvSUqC8eOdeEQ3t3hESkr5RsJViDak3k1gFl0aMKCk\nl1Pb5m3p0qILzXpu4JtvTIvMYvFEVY3EFX6RwlIhwewXrYluSUkwfEwWm45tYmQXZwb7jAwzMdHF\n5Yz7DPZxEhUxZsw5Lqdx3cax+kgyF110TriiytQZ/fxEsOvnDVX9Wl73ixQWSxU5fhz27oXc1ssY\n2nEozUKbmRPLlxv3SljY+TcFe0zCU0sCyo9L2PESFi+o6tcSW/klFl8TzH7R6uq2ZImpHKeklhkf\n4SkeAQ1znISLSy+FdetK+ryO7jqaNQfXcNnYszWKSwTzuwnBr583VPVr+cYvUlgsVcTj+IiUFLPQ\ndXk05JZEixbQqxesX292m7Sgb5u+FHVYzQ8/wKlTtSSjpd5R1a/lb36RwlIhwewXra5uixfDoJHH\n2Z+xn6Edh5qDmZmwY0f58QgISOC6ToyTcDF6tGmCOYzvNp7k/QsZNeqcw1UimN9NCH79vMEOprPU\nO/buNb05jzRZwuiuo2kc4iyLsmKFWWgnPLz8G4O9JVEZ48efMzBiUs9JLPxhoWmR2fESFg/YLrD1\ngGD2i1ZHN5eradEPC5nQfULpiYpcTRD84yS8aUl8/XXJeIkRnUawO203g0edqLaRCOZ3E4JfP2+o\n6tfypF+ksFiqwOLFZqnSBT8sYFLPSaUnFi0ytWVPNMT1JNyJjITBg02LCwhtFEpCXAKHwheRng4H\nDlR8u6VhUtWvZbBfpLBUSDD7RauqW3Gx6bLZZej3hDYKJT4m3pw4dgz27/ccj4CGPU7Cxfjxxso6\nTOoxiUV7FzB2bPVcTnVOPx8T7Pp5Q1W/lml+kcJi8ZItW0xHne9yFjCpxyTEVfAvXgxjx5pZTz3R\n0FsScJ6RmNhjIgt/WEhiotrxEpZysTGJekAw+0WrqtvixaacW/CDMRIlLFwIEydWfHMAFh2qUzEJ\nMIH9H36AkycB6BHTg+ahzel00WaSksAsGe89wfxuQvDr5w1VNRJD/CKFxeIlSUkwamwuqw+uJrFb\nojmoauIREyZUfHOw927yxkiEhpoAtluzYVKPSWzNX0DjxqYHscXiTlW/lvV+kcJSIcHsF62KbgUF\nJuYa2nMZg9sPpkWTFubE999D06bQo0fFCTTU9STKMn68MaoOpivsgmp1hQ3mdxOCXz9vsO4mS71h\n7Vro2RNWHy/H1VRZKwKCvyXhLZMmwfz5Jb6lhLgE1h5ay8ixOXa8hOU8qvq1fOUXKSwVEsx+0aro\n5urhel7XV2+NREOeu8md3r3NBIibNwMQFR7FkA5DaNJ7GSkpUFjofZbB/G5C8OvnDV5/LSLSBPij\nH2WxWCpkwQIYNCaVE7knGNLBCY/l5JiV6CoaH+EiAIHrWsVbIyECU6fCl1+WHJrUYxLr0hbQpYsZ\nb2exuPBoJEQkRESuFZGPReQQsBfYJyKHROQTEblGxNu30lITgtkv6q1uaWmwdSukt1rAhO4TCBHn\n1V282PTYadGi8kSCfZxEVT7Hyy+Hr0odA5N6TDIttEnGGHtLML+bEPz6eUNFX0sKcBHw/4DuqtpB\nVdsD3Z1jFwNLa5K5iEwWke0isktEHi7n/M0isklEvhORlSIyoCb5Weovixc7nXL2l4lHzJ0LV3i5\nFpYdJ1HKmDFmSdO0NAAGdxjMydyTDBqTWiUjYQl+RD10jBaRcFU9W+HNXlxTwb2NgB3AeOAQsA64\nSVW3uV0zAtiqqpkiMhmYqarDy0lLPelhCQ7uvBMuHFDAH/Lasu2X22gf0d4Mv+7UySym07Nn5Ym8\n/TbcfrsZmd2li/+Frm169IA9e7wf7HDllXDzzfDjHwNwy6xbGN5xFI9NvIt9+yAmxn+iWuoGIoKq\nVli78FilchX+IvJOOQm/435NNRkG7FbVfapaAHwAXFVGhtWqmunsrgU61SA/Sz1F1cSmYwavID4m\n3hgIgI0bISrKOwMBtiVRlqlTz3E5XdnrSubvmctll50zKNvSwPHma7nQfUdEGmPcUDUlFnCfUuwg\nFa98dycNtHdVMPtFvdFt2zYTa950Zi5X9HJzLc2da2rD3mJjEudy+eUwb15Jd6ZJPSexbP8yxk7M\n9drlFMzvJgS/ft7gcaIbEXkMeBRoKiJZbqcKgL/7IG+v/UMiMhb4KTDS0zXTp08nLi4OgOjoaAYN\nGlTSfc31oOvr/rffflun5Knt/b/9LYX+/eHLXXP573X/LT0/dy48/7z36TmFaMqaNRATU2f089m+\nS7+q3N+lCykvvwyDB5OQkMDQjkM5dOovzJ59KaoJiNQh/ex+jfdTUlJ48803AUrKy8rwGJMouUDk\nWVV9xKvUqoCIDMfEGCY7+48Cxar6pzLXDQBmAZNVdbeHtGxMIoiZPBmuuH0nzxwZy8EHDppJ/Q4c\nMNNeHzlipprwhg8+gJtuguPHoU0b/wodCHr3hp07qzYB0//9n/kNX34ZgBdWv8C2k9tZdO/f+fJL\n6NfPT7Ja6gQ1ikmISHeAigyEiFQyD0KFrAfiRSRORMKAG4HZZdLvgjEQt3gyEJbg5swZMwzidPsv\nuTz+8tJZXz/5BK66ynsDAXbEdXlcey3MmmU6AWDiEl/unMvESWp7OVmAimMSz4jIXBH5HxEZIiId\nRCRWRC4SkRki8iXwdHUzVtVC4B5gAbAV+FBVtzlpz3Au+z3QEnhNRL4RkQY5zMfVXAxGKtNt+XIY\nMACSDszl8vjLS098/DFcf33VMrMxifPp0weio0tG0MW3iicyPJL40Ru9MhLB/G5C8OvnDR5jEqp6\no4j0BH6MMQZdnVP7gRXAvaq6pyaZq+o8YF6ZY2+4/f8z4Gc1ycNSv1mwAEZPyOTlQ18zrvs4c/DA\nATNd6bhxVUvMZRyCtSVR3bGt110Hn34Kw03v8it7XUla8VxWrbqIM2fM3ImWhkuFX4vj4nkeWAzs\nBLYDi4AXamogLN7jCkAFI5XptmABRA5axKguo4gIizAHZ82CadPM/ENVwa4nUT7XXmuMhBPLuKLX\nFSxKncOAAaYlVxHB/G5C8OvnDd5Uqd4G+gJ/BV5x/n/bn0JZLACHDpmY6tai2VwR79b19eOP4YYb\nqp6gbUmUz8CB5jfZsAGAkZ1Hsid9DyMmHrZxCYtXRqKfqt6pqktUNdlxAdk+D7VIMPtFK9Jt/nxI\nnJDPl7vmcnWfq83BQ4fMwAlvJvQrS7DHJKqLiBl5/e67AIQ2CmVSz0mEXfil+1i7cqkX+tWAYNfP\nG7z5WjY602MAJV1XN/hPJIvFMHcudE9MoXfr3sRGOeMs338frr666q4mCP7eTTWZb/OWW0wXYWdg\n3bRe0/g273PS0sxqp5aGizfjJLYDvTCjoxXogplzqRBQVQ34pHt2nETwkZcH7drBNf+6i34devDg\nyAeNz/zCC+H11+Gyy6qe6Jw5JpZRUACNPfbZqL/07w9btlR9oWoXw4fDE0/AlCmcPnuaTi90Ytru\nAwwb2IL77vOtqJa6QY3GSbgxGTPz6xggwfl/CnAlMK2GMlos5bJ0KVzYv4j5+z7n2guuNQc3bDDW\nY9So6iVqWxIVc+ut8I6Zqi0qPIqEuARaXzqXuXN9IJul3lJpdUpV99WCHHWeQ4dgzRpYvx5SU80M\nyyJmGYO4OBgyxFRu27f3fd4pKSlB28vCk25z50L/qavJiWhPjxhnzOabb8L06dUvDAMUk6i1Z1dT\nI3HjjfDYY5CVBZGRXN/3ej7e/AmrV9/sOnQewfxugu/1Kyw05ci6dSa0dvQo5OdDeDjExkKvXqYc\nGTSo7qyNFaRVKt9w8iQ8/7xZ02bgQFNGNW0KU6bAvffCL39p5khr1sxUwPr2NQ/4X/8yFV5L9VA1\nRiK366zSVsTZs8ZnfuutNUvY4pnWrSEhwfQew4yXWHogiYtHZrNoUWBFq+98+y38z/8YF+p995k4\nz5AhcMcd8KtfmbpP375m+M9tt5lZY37+c1i5sg68tqpa7zejhu9ITVW95x7Vli1Vp09XXbhQtaCg\n8vvOnlWdPVt1yhTVDh1UX31VtbDQp6I1CLZsUe3StVi7vthVNx/bbA5+/LHq2LE1S3j2bFUfvyt1\nioEDa67f3LmqQ4eW7E5+d7JOf+5D/elPayhbA2XLFtUrrlDt1En16adN2eINBw6oPvOMaq9e5nHM\nnq1aXOx7+Zyys8Ly1bYk3MjJgd//3jT1mjeH77+H//wHJkzwLs4ZFmZmrv7qK7N9+CFcdJFpXlq8\nZ+5cuOTqjYQ1CqNfG6e39euvm5WHLJ7xxWrCkyebJvS6dQBcf8H1HGv1CV9+WTK9k8ULcnPh/vth\n7FhITITdu40nr3Nn7+7v1AkeecS4pB55BB5/HEaONEuo1DbWSDgsWWJmvNy92zQNn30WOnSofnqD\nBpk0H3nEzEP39NNQVFS9tIK5r3Z5us2dC9r3I67ve72Z0G/bNtNrpzoD6NwJQLu9zs/dVJZGjeCu\nu+DVVwG4qs9VrDy2gOg2ua6xducQzO8mVE+/DRtM5fD4cdi+HR54wMQcqkNIiJk15Ztv4Kc/Na7u\nX/3KGKHaosEbidxc86Pfeiu89prphu+tta8MEbMy5IYNsGiRqaRlZPgm7WDl1CnY9F0xa7L/y00X\n3mQOvvqqcehWZ2yEOwF37voZXxgJMKXRZ5/BqVO0btaaizteTJ8r5tteTl7w3numIP/9701Z4qsl\nYENC4Gc/M/WlEyeMEaq1VkVl/qj6sFFNP+zu3ar9+6v++Meqp05VKwmvKSxUve8+1b59Vfft829e\n9Zl331UdedNyvfDVC82BzEwTHDp4sOaJf/ZZcMckhgzxnX633KL63HOqqvqPDf/Q0a9cp0OG+Cbp\nYKS4WPWxx1S7dTNxCH/z3nuqbdqoPv98zWIV2JiEZ+bNg0svhRkzfGvxPdGoEfz1r6ZCfOmlsHWr\nf/Orr8yZA40Hv1/ainjjDdMEi61oZVsvsS0J7/nVr8wLm5/P9X2v59usRew5nMHBg77LIlgoLoa7\n74bkZDPjem0s1PSTn5i83n3XDJb3p/upwRkJVbMY189+Zia+/OUvffttVcavfgV/+pOZeuj77727\nJ5j9vu665eXBvIUFbCn+2BiJvDx48UUT2Kmn1LuYhIuhQ+GCC+Ddd4luEs347uO54LpP+fzzcy8L\n5ncTKtevqMh0Y92+HRYuNL2Ia4u4ONNFNiTEBLVTU/2TT4MyEsXFpo/yxx+bzhvVHbhbU265BZ57\nzvSa2r49MDLURRYvhi4Ji+jVOp5uLbuZgSkXXWRWHfIFwd6S8DWPPmpqNEVF3NL/FrK7vcesWYEW\nqu6galoQhw6Z3ozlDTb0N02bwttvm/kZR46ETZv8kEll/qj6sOGFH7agwLhZR41STU/32mXnV/7z\nH9W4ONXDhwMtSd3gjjtUL3r6J/rSmpdU8/LMj7Nype8y+OST4I5JXHyxb/UrLlYdPlz1/fc1ryBP\nY56N0YiOqXrihO+yqM/89rdmDMPp04GWxPDhhyZOsWiR9/dgYxKGvDyz0uXJk2YRm+joQEtkmD7d\ndP2fOhVOnw60NIGlsBA+X5DOTubyk/4/MeMiLrzQBHB8RbC3JHztNxUxvtnHHye8WLiu73V0m/Zf\nZs+u/NZg56WXjEciUC2I8vjRj8zS726zvvuEoDcSWVlm6ozwcPjiCzOFRl3it7+FSy4xRsyZpfk8\ngtnv69Jt2TKIHPEBk+Mn0aqgsSmcnnnGt5nZcRJVZ+xYM6HQ3//ObQNv42Tnf/PprNLfMZjfTShf\nvzlzjBdu4UIzfUZdYvRoMz7r0UdNl35fENRG4tQpEyDu0cP0YKppN3t/IAKvvGKCTw89FGhpAses\nWVA04F/cOfhOM5JxyhTTkrB4j796YDz7LDz1FCMj+hIVGcKSH1Y02Jbvtm2m9f/pp9C1a6ClKZ++\nfc0sys89Z7YaU5k/qj5slOOHPXRItV8/1Qcf9M+cJ77m1CnVHj3MOIGGRlGRapsLv9UOf+6shd9t\nMo7VI0d8n9GHHwZ3TGL4cP/pd/fdqjNm6POrntfYe27V//7XP9nUZdLSVOPjVf/970BL4h0HD6r2\n6aP6u995LgNpqDGJPXtMz6WbbzbNwtrs4lpdYmLMINf77zdD8BsSX38NRQP+w8+H3E6ju38BTz7p\nnznXbUyi+vzf/8Hs2fz0TB/S2szmv5+l+y+vOkhxsSlPpk41XV7rA7GxpkUxZw78+tfVf/2Dzkhs\n2WL8cg8+aPxytWkgVJXMvExSM1PZnbabrSe2svnYZn5I+4Fj2cfIyc9xtXzKpX9/+NvfzFwt7tN3\nBLPfNyUlhfc+yiUv/j3uW11s3uQZM/yTmY1JVJ/oaPjrX4m+636ujh3PwmPvkp0d3O8mlOr33HOQ\nmVm5+6ZYizmRc4J9GfvYeWonW45vYcvxLezL2MeJnBPkFdbuGgJt25oYxdq1ZiBvdeaPC+gajiIy\nGfgL0Aj4p6r+qZxrXsKshJcLTFdVj/XstWvN6pQvvmhGJPqagqIC9mXsY1faLnan7WZ32m5+SP+B\no9lHOZ5znOM5x2nSuAktwlsQ1iiMsEZhhEgIuQW5ZOdnc/rsacIbhxMXHUf3lt0Z1G4QQzsOZVjs\nMLdOrVYAAB4gSURBVNo0NxGwH/3IWP8ZM8zyCfWhFVQTiorg7U3vcNvwC2j16j9Ms8JfiwLZlkTN\nuOEGmD+f5786wJxL3uCLL+7xyUD4us6qVfDCC2bBsdBQc6youIhNxzax+sBqNh/fzJbjW9ibsZcT\nOSeICo8iIiyipAxQlJz8HLLzs8nKz6Jp46Z0jOxYsnVt0ZWeMT2JbxVPfEw8rZu1NhNb+ojoaBNk\nv+oqs1bFm2+W6uENla5x7S9EpBFmrezxwCFgHXCTqm5zu2YqcI+qThWRS4C/qurwctLSpCTlxhvN\n1N5XXFF9ufKL8tmbvrfEEOw6tYvd6cYgHDx9kNjIWHrG9DQPNSae7i270zGyI+0i2tGmWRuahjb1\nmLaqkp6Xzr6MffyQ9gMbj2xk/ZH1rDu0jvhW8UztOZWr+1xNn+hBDB8u3HuvGRkezCQnK9M/7sOO\nL7No+sJLppuXv/jvf03tIViNxWWXwYoV/tUvJwcdOpRHemeytNnbrHl/vP/yqgOkpZnFgV5+GcZM\nPM0X279g1vZZLN23lPYR7RnVZRQD2g3gwrYX0qNlD9pFtCOskeceMqpKRl4Gh7MOczjrMIeyDpVU\nPHed2sWutF2oaonR6NnS+euUOW2atam2ATlzxnxeYWGmAhoe7t0a14E0EiOAJ1R1srP/CICqPut2\nzevAElX90NnfDoxR1WNl0tI2bZSPP4YxYyrON68wzzyc04c4lHWIg6cPnvOQDmUdonNU5xIjUPKw\nYnoSFx1X4QtQXQqKClh5YCVf7fqKj7d+TFR4FFM73MHff3kbyxfG0LdvOTdlZ5u1D48cOf/v8ePm\njTh71myFhWZoZtOmpg9wu3bGYdmpk+n6NWBAwPryXT1jNk98+WMG3fEb5I9/9G9m779vHMvWSNSM\nXbvIGXExPx7Ukzc/XE+rVv7Nrlzy8sy8/tu2wa5dcOyYee9d735hoWmmipjFYZo3h4gIU61u3958\nA+3bn7s1b35OFqpw9TVKePwyGPY3FvywgNFdR3ND3xuY0H0CHSJrsJZABZzKPVVSHu1O211SSd2d\ntpuCooJzDEiPmB7ERsbSMbIjHSI70KppqwqNSH6+qSfl5JgeWs2b120jcT0wSVV/7uzfAlyiqve6\nXTMHeEZVVzn7i4GHVXVDmbR09lWjiGxSAAWFSGEB5BdQdDaP4vw8ivPPovn5aP5ZpLCQ5oQTQRjN\naEwTCSU0rAnh4RE0adKcJk0iCAkNM6sMhYaagjUiovQlK+9v8+am8HX9dW1Nm3rnOikuNjN0ZWdT\nfDqTjZsXsmzdJ+zZtp6O+wYxIDaSKyKizjUGRUVmwYv27c//27atybtJE1NdaNzYfFS5uWY7dgwO\nHjTbrl3w3Xfm2sGDTUEzZoyZu8fPfYZzj2fwer8OTB46jL5fpfjfXfLee2ZOlFp852t1DejRo2H5\n8lrR7+yKpWRNTOT/JvyUF774h9/zY98+M5hm1Sqz7doF3bpBnz4QH2/e/bZtTWWneXMzo2bjxubb\nyskp3dLSzPt/9GjpX9c3FRpa8h0Vt29P0tFM3k5bR8eBYYwcdh1jht9Ii/Zdzei5iAjvfTZFRaay\n5v4NuracnCrtF2Sf5mxOJmcK88gryiOvMI+zRfmcLS4gvyifAoqQsDAkLBwNDzPfcHgTQsLCITwc\nDQulKDSUbT+EklUYxkPfLKrTRuI6YLIXRuJZVV3p7C8GHlLVjWXS0kk92tG+hXk5IpqGEx/bhhG9\ne9KkaSTfHUojPLw5Ey66mKiIVizbvgMaNSJh2DBo3JiUdeugqIiE/v2hsJCUDRvMfp8+cOYMKRs3\nwpkzJLRvDzk5pOzYAXl5JEREQHY2KYcPw9mzJDRqBLm5pGRkmPMFBdCkCSmhodC4MQlhYaBKSn4+\nqJIQGmquz8mB8HASWrSAiAhSwsKgZUuG9uzKO9v28GHWRoYPGcwjt/+W6Lg+pOzeDc2akTB2LFAa\nXHMVRlXeX7IETpww8ixbRsrcuXDwIAnjx8MVV5ASEwNt2lQ//fL2T56kz8NP8Dz7mfr3L5BGjXyb\nfnn7w4bB7NmkOD2n/J5fQsI5gV2/59e1K+zfjytHf+e3YcE8erzwItG/+V+YONG36RcWmu/pyy9J\n+egjyMoiYeJEGDmSlPBw6N6dhAkTfJefKgmDB1N85DAvvflnVm6cS1x2FL2bdKZH41AkLZ2EvDw4\nfZqU9HRTHjRubL7X4mIQISE8HEJCzPddUEBCcTHk5ZFSUABhYSQ4lccUgKZNSWjb1uzn5kKTJiR0\n62b2T540+/36mf3UVLN/8cVmf8sWI/+QIaY8cRaWSBg0iPyCPOauXEJObiaDurQl70wWa7Zsp/Ds\nGS5sG8F3+46x8PtUKC6meWEon6UeqtNGYjgw083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+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x7fc30d2b9150>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "from __future__ import division\n",
+ "from numpy import arange,sinc,sin,pi\n",
+ "%matplotlib inline\n",
+ "from matplotlib.pyplot import plot,grid,title,show,xlabel,ylabel,legend\n",
+ "\n",
+ "#Figure6.7(b):Practical Solution for Intersymbol Interference\n",
+ "#Raised Cosine Spectrum\n",
+ "\n",
+ "rb = 1 # The bit rate\n",
+ "Tb =1/rb#\n",
+ "t =arange(-3,1/100+3,1/100)\n",
+ "Bo = rb/2#\n",
+ "Alpha =0# #Intialized to zero\n",
+ "x =t/Tb#\n",
+ "p=zeros([3,len(t)])\n",
+ "for j in range(0,3):\n",
+ " for i in range(0,len(t)):\n",
+ " if((j==2) and ((t[i]==0.5) or (t[i]==-0.5))):\n",
+ " p[j,i] = sinc(2*Bo*t[i])\n",
+ " else:\n",
+ " num = sinc(2*Bo*t[i])*cos(2*pi*Alpha*Bo*t[i])\n",
+ " den = 1-16*(Alpha**2)*(Bo**2)*(t[i]**2)+0.01\n",
+ " p[j,i]= num/den\n",
+ " \n",
+ " \n",
+ " Alpha = Alpha+0.5#\n",
+ "\n",
+ " \n",
+ "plot(t,p[0,:])\n",
+ "plot(t,p[1,:])\n",
+ "plot(t,p[2,:])\n",
+ "xlabel('t/Tb------>')\n",
+ "ylabel('p(t)------->')\n",
+ "title('RAISED COSINE SPECTRUM - Practical Solution for ISI')\n",
+ "legend(['ROlloff Factor =0','ROlloff Factor =0.5','ROlloff Factor =1'])\n",
+ "grid()\n",
+ "show()\n",
+ "#Result\n",
+ "#Enter the bit rate:1\n",
+ " "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Example 6.9 Page 254"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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jUcH93n0XVltt2QRyHMdpydTVhfAef/lL6REmsswnsQ0w2Mz2jMtnAouSzmtJ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\njuM4qfw/WG7FB0YOBAAAAAAASUVORK5CYII=\n",
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x7fc30d372dd0>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "from __future__ import division\n",
+ "from numpy import arange,sinc,sin,pi\n",
+ "%matplotlib inline\n",
+ "from matplotlib.pyplot import plot,grid,title,show,xlabel,ylabel\n",
+ "\n",
+ "\n",
+ "#Caption:Frequency response of duobinary conversion filter\n",
+ "#Figure6.9:Frequency Response of Duobinary Conversion filter\n",
+ "#(a)Amplitude Response\n",
+ "#(b)Phase Response\n",
+ "rb = 8 # the bit rate\n",
+ "Tb =1/rb# #Bit duration\n",
+ "f = arange(-rb/2,1/100+rb/2,1/100)\n",
+ "Amplitude_Response = [abs(2*cos(pi*ff*Tb)) for ff in f]\n",
+ "Phase_Response = [-(pi*ff*Tb) for ff in f]\n",
+ "subplot(3,1,1)\n",
+ "plot(f,Amplitude_Response)\n",
+ "xlabel('Frequency f---->')\n",
+ "ylabel('|H(f)| ----->')\n",
+ "title('Amplitude Repsonse of Duobinary Singaling')\n",
+ "subplot(3,1,3)\n",
+ "plot(f,Phase_Response)\n",
+ "xlabel(' Frequency f---->')\n",
+ "ylabel(' <H(f) ----->')\n",
+ "title('Phase Repsonse of Duobinary Singaling')\n",
+ "show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Example 6.15 page 259"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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KOOOpmX1lZgMKa5aKy9KlcMQRbg7Xc86JW00gDs44w00p2Leva2JaTjzxBNx5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\n",
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x7fc1e165f590>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "from __future__ import division\n",
+ "%matplotlib inline\n",
+ "from matplotlib.pyplot import plot,grid,title,xlabel,ylabel,show,legend,subplot\n",
+ "from numpy import arange,cos,pi,sinc,sin\n",
+ "\n",
+ "\n",
+ "#Caption:Frequency response of modified duobinary conversion filter\n",
+ "#Figure 6.15: Frequency Response of Modified duobinary conversion filter\n",
+ "#(a)Amplitude Response\n",
+ "#(b)Phase Response\n",
+ "rb = 8# the bit rate\n",
+ "Tb =1/rb# #Bit duration\n",
+ "f = arange(-rb/2,1/100+rb/2,1/100)\n",
+ "Amplitude_Response = [abs(2*sin(2*pi*ff*Tb)) for ff in f]\n",
+ "Phase_Response = [-(2*pi*ff*Tb) for ff in f]\n",
+ "subplot(3,1,1)\n",
+ "plot(f,Amplitude_Response)\n",
+ "xlabel('Frequency f---->')\n",
+ "ylabel('|H(f)| ----->')\n",
+ "title('Amplitude Repsonse of Modified Duobinary Singaling')\n",
+ "grid()\n",
+ "subplot(3,1,3)\n",
+ "plot(f,Phase_Response)\n",
+ "xlabel(' Frequency f---->')\n",
+ "ylabel(' <H(f) ----->')\n",
+ "title('Phase Repsonse of Modified Duobinary Singaling')\n",
+ "grid()\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
+}