{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# 离散时间信号\n", "\n", "## 生成序列\n", "\n", "### **单位样本序列和单位阶跃序列生成**" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "% Generation of a Unit Sample Sequence \n", "clf;\n", "% Generate a vector from -10 to 20\n", "n = -10:20;\n", "% Generate the unit sample sequence\n", "u = [zeros(1,10) 1 zeros(1,20)];\n", "% Plot the unit sample sequence\n", "stem(n,u);\n", "xlabel('Time index n');ylabel('Amplitude');\n", "title('Unit Sample Sequence');\n", "axis([-10 20 0 1.2]);" ] }, { "cell_type": "raw", "metadata": { "raw_mimetype": "text/restructuredtext" }, "source": [ "\n", ".. note:: 问题:\n", "\n", " 1. 根据上述产生单位样本序列的方法,编写单位阶跃序列的代码;\n", " \n", " 2. 试分析下面几个函数的功能:\n", " ``axis, title, xlabel, ylabel``\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### **指数信号**" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "image/png": 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AaJwwhsQjrDQCgEYILSQAAOACEhIAAHABCQkAALiAhAQAAFxAQgIAAC4gIQEA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\nQRBGjRrl7e0tCML69evZcX6qsboIuapGZtasWYI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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "% Generation of a complex exponential sequence\n", "clf;\n", "c = -(1/12)+(pi/6)*i;\n", "K = 2;\n", "n = 0:40;\n", "x = K*exp(c*n);\n", "subplot(2,1,1);\n", "stem(n,real(x));\n", "xlabel('Time index n');ylabel('Amplitude');\n", "title('Real part');\n", "subplot(2,1,2);\n", "stem(n,imag(x));\n", "xlabel('Time index n');ylabel('Amplitude');\n", "title('Imaginary part');" ] }, { "cell_type": "raw", "metadata": { "raw_mimetype": "text/restructuredtext" }, "source": [ "\n", ".. note:: 问题:\n", " \n", " 1. 哪个参数控制了序列的增长或者衰减率?\n", " \n", " 2. 哪个参数控制了序列的幅度?\n", " \n", " 3. 如果将上述代码中的参数 ``c`` 修改为 ``(1/12)+(pi/6)*i`` ,结果是什么?\n", " \n", " 4. 函数 ``real`` , ``imag`` , ``subplot`` 的作用是什么?\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "% Generation of a real exponential sequence\n", "clf;\n", "n = 0:35; a = 1.2; K = 0.2;\n", "x = K*a.^n;\n", "stem(n,x);\n", "xlabel('Time index n');ylabel('Amplitude');" ] }, { "cell_type": "raw", "metadata": { "raw_mimetype": "text/restructuredtext" }, "source": [ ".. note:: 问题: \n", " \n", " 1. 哪个参数控制了序列的增长或者衰减率?\n", " \n", " 2. 哪个参数控制了序列的幅度?\n", " \n", " 3. 如果将上述代码中的 ``^`` 和 ``.^`` 的区别是什么?\n", " \n", " 4. 将上述代码中的参数K修改为20,参数a改为0.9,运行的结果是什么?\n", " \n", " 5. 上述代码产生的序列长度为多少?这个长度是通过哪个参数来控制的?\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 简单的序列运算\n", "\n", "### 信号平滑" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "image/png": 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v+TZaZpQH28UKqlYVNd+zKa+3fGy+tVsARiMclXTOZZEovyMlOdX8F8S+fLBp\nRCujXAW45kK+0SqF4JvMgLLJCaVTAtRk8Ivy+ditmzcN1wQrXrZ6RIN+fFdGKVL2pQE+d97ZK1K+\n4VVR42GvwmK5+u41GmG3Q6fz/zggpcSU2MpgCTUnx/stsIHSEnsbswYAXPsVx/dhme83Cvad+9rY\nBEHrCQmehrd06dJ77rnHL6f64IMP4uPjL126pGwxGAwvvPBC+SMdHkfkzsiqzuVyeSwWD8d5LJaq\n9jocVZ3B4fAexvNVHVktl8tjtXpPxUKqw9mqeEU1eTlVBMbzHo7zCMLVCHneY7F4XK4aXb3qgIGy\np/IjFj8LvurX7nJ5eN7D83W/EPtXtwgFwVuwguCxWq/Za7F4S0kpdnZwta/I4fD+4ljxsmfV/LdT\nfw6HN86qlXmP+b58RnmTlFfbt1zVYdT2D9D3PV/tr569TOWfw3H1H3vnsE8AQfDw/DW/bovlmqfU\n8G2m/HHxvG+RxsfHV/9c9QQiIf3www833XTT1q1b638qlpAuXryobLnrrrvmzJlT4cHxPpYuXVrx\nGdnHEMdd8zt2OCr9A6jsJMpfVA2fxd5VFkvZN199sppyZvaHrbyi2r6cyk7L/mCq/Viv+jOizCds\nYD4flagq+xCv4iMvkJS3BHs/KEVUvtjLvOXKfEL5frCWeRZ7Sv3fZtW+kDpcyPdFKZ/FwDVv5gqf\nVfUbSfntA94iVf6xryCCcLWoG+KLoPKrFISr/9il2Yv1/ZLnlw8BnwB2PvHEzsjIX5o2XdqunUGn\n03hCCkSnhv3794uimJ2dbTAYOnTo4Lvrueeea926dc1PtXr16jlz5mzatIlNE+52uxMTEydOnDhr\n1qzyB9eiP4lvqwJrPqpJQ1l5SmeBMpVluXRFTTZPFkqnaWRtFH5vSPFtagfq/nL8EgC79cXaQJSO\nIYG5CVdZVL5Ni+yOEQBl/IgWlGn3q/pIduONtUw6nbBYvC+wwuJlDUGiWHH7qtIqqHQcqMPdOOWv\nqc5vbKXRFahRI7Dym/VtvivTFKy83yrsGVT/m3ll/uiUd3sD/Y3XVmmb5xCdbsvRoyoHU7lAJKSV\nK1e+WknL5ZYtW9q3b1/zU+3fv//hhx9esGABmx38u++++7//+79XX331oYceKn9w7To4+jZS1/Pd\n4/vB53sHXlloMzB9tNhHFbsDpAr2Wc/+7DXyZ4lrfzsc55/fuOrk0vVca7geeJlvDOyOoM3mvaHF\nflnsTomSn9jvUclP8PlA951OgBWsVY1JI1kiNJuRk1NBHgoM5cYPtPFuL4e6fVds//79qamps2bN\nqlW375KSkhEjRpw/f37+/PmtWrVauHBhfn7+pk2bYmJiyh+sctH7/g0TrVE6YiyvtgAAIABJREFU\nB9ahF0aj4dvfAZXMReN7sG+vM6XLHK6tyanb8Uz5Ehb4+neQoIR0jTNnzqxZsyY1NfXQoUMAtm/f\nXqsaEoDs7OyZM2eyp8fFxb3yyiuDBw+u8EiNFz0hhASYxj8VA9HtG4DH48nMzExNTd28eXNxcXFE\nRMTdd989atSo2NjY2p4qISFh3bp1p0+fdrvdtU1mhBBCNKvBE9Jvv/2WlpaWlpb2yy+/sC0DBgxI\nTk6usJGt5uqQyQghhGhZAyakPXv2vP/++1u3bnW73V26dHnyySdHjhz5+OOPX3fddfXMRoQQQhqf\nBkxI6enpkiT169dv1qxZffv2bbgLEUIIaQTqO/d2FXr06NGsWbO9e/fOnz//gw8++O233xruWoQQ\nQoJdAyYkk8m0bdu2OXPmAFi0aBHP8yaT6cSJEw13RUIIIcErQN2+s7KyUlNTMzIyLly4EBkZed99\n9z344IP9+/cPCwtruItqvIMjIYQEmMY/FQM6DqmwsPCrr75KTU3dt28fgE6dOn3xxRdRUVENdDmN\nFz0hhASYxj8VG7DJrrzIyMgxY8Z8+umnX3311eTJkwsLC4uLiwMZACGEEM1SbeogAG63u0mTJg13\nfo1/FyCEkADT+KdiQGtIZTRoNiKEEBJc1ExIhBBCiIISEiGEEE2ghEQIIUQTKCERQgjRBEpIhBBC\nNIESEiGEEE2ghEQIIUQTKCERQgjRBEpIhBBCNIESEiGEEE2ghEQIIUQTKCERQgjRBEpIhBBCNIES\nEiGEEE2ghEQIIUQTKCERQgjRBEpIhBBCNIESEiGEEE2ghEQIIUQTKCERQgjRBEpIhBBCNIESEiGE\nEE2ghEQIIUQTItQOoC5ycnJ27dqlPAwPDx8zZoyK8RBCCKm/oKwhZWRkvPTSS8tLvffee2pHpFEp\nKSlqh6A+KgRQIQCgQggGYR6PR+0Yau3pp58GkJycXPVhCQkJ2dnZAYlIo6gEQIUAgAoBABUCAM0X\nQlDWkLKzs3v16gUgGLMpIYSQCgVfQioqKjp27NiWLVuSkpL69u1rNBq1nPAJIYTUUPAlpCNHjrjd\n7mbNms2bN+/5558/fPjw5MmTCwoK1I6LEEJIvWj9HpLT6Xz99deVh6mpqR6P5/DhwzfffHNERASA\nPXv2jB8//rXXXhs9enSZ506cONG3Mx4hhIS4pKSkjz76SO0oKqX1bt/XX3/9sGHDlIcRERHNmjXr\n06ePsqVfv37h4eE5OTnln6vlcieEEFKG1hNSz549WZ86xfLly20229atW5s3bw7g6NGjJSUlcXFx\nKgVICCHEP4LvHtLgwYPPnTs3Y8aMn376ae/evbNmzYqKiho+fLjacRFCCKkXrd9DqlB6evrrr79+\n6tQpAL169VqwYEHv3r3VDooQQki9BGVCAuDxePLy8tq0adOmTRu1YyGEEOIHwZqQCCGENDLBdw+J\nEEJIo0QJiRBCiCZovdt33ezbty8zM/Py5cuJiYl6vV7tcALtzTff/Mtf/tKzZ09lS0gVyK5du/bs\n2eN2u/v37z9gwABle+gUgtPp3L9/f5MmTZKSkpKSkpTtoVMCitOnTy9fvnzy5MnXXXcd2xI6hVDF\nMj2aLYQmFotF7Rj87MMPP5wxY0Zubu6xY8fsdvvvv/8+ZMgQtYMKnPT09IULF9555506nY5tCakC\nee211+bPn3/q1Kns7Gyr1Xr69Gn29xY6hWCxWF599dULFy5kZmZ+8sknJSUlLCuHTgn4eu6559LT\n04cPH84SUkgVwieffLJ48eIDBw7s3r179+7d+/btmzhxIjReCJ7GpaCg4JZbbnnyySfdbrfH43n9\n9dfj4+N/+uknteMKhCVLlowePTo+Pj4+Pv6///0v2xhSBZKdnZ2QkPDGG294PJ6SkpKXX345Pj5e\nluXQKYTDhw/Hx8e///77Ho/nypUrkyZN6t2795UrV0KnBHylpqb26dMnPj7+u+++84TY34LH43nq\nqaeeeuqpMhs1XgiN7R7S7t27i4uLJ0+eHB4eDuDRRx8FsG3bNrXjCoTo6Ojbb7/94Ycf9t0YUgVy\n4MABj8fDXmNYWNj9998P4OjRo6FTCAcPHoyMjHzkkUcANGnSZPDgwZcvX75w4ULolIDi2LFjr7zy\nyrx585QtoVYIFS7To/FCaGz3kHJzcwH06NGDPWzbtm3btm3ZxkZPEAQA+fn5q1atUjaGVIEMHDhw\nxYoVMTEx7OG+ffsAdOnSZceOHQiNQhgxYsSIESMAnDp16sCBAx9//DHP81FRUSH1NgDgdrtnzZo1\natSoO+64Q9kYUoWgLNNjtVqLi4v79Onz4osvJiQkaLwQGltCunTpEoDIyEhlS2RkZFFRkXoRqSyk\nCqRjx44dO3ZkP2/cuDE5OXnIkCE9evRwOp0ImUJgHnrooby8vKZNmz7xxBMIsbcBgPfee+/kyZOz\nZs06e/assjGkCsF3mZ6CggLWs2P9+vUaL4TGlpAYt9vt+zOrnIaykCqQgoKCV155hd3KXrhwobI9\npAohLS3t5MmTb7755iOPPLJmzRq2MURK4IcffnjnnXfsdntkZKRvQmJCpBA4jvvss8+UZXoSEhLG\njx//3//+l+3VbCE0toQUHR0N4I8//ujatSsAt9t96tSpqKgoteNSTagVyE8//fTYY48BWLJkCWu8\nQigVwu7du0+fPj1s2LB27dq1a9du0aJF/fv3lyQpNjYWoVECAFasWNGkSZP33nsPAPvuv3DhwhEj\nRoTO2wBAq1atKlymp3PnztBwIWglMfoLaxvduXMne7h///7i4mKlwTQEhVSBnD9/fsqUKRzHZWRk\nKNkIoVQITqfzmWeeOXHiBHt45coVAGFhYaFTAgDuvPPOsWPHdu3atWvXrqy3d8eOHdu2bRtShbB8\n+fKkpCTWQAefZXo0XgiNrYZ06623chy3ZMmSqKioVq1aLVy4MCoqSkO97AMupApk9erVJ06cmD17\n9o8//qhsTExMDJ1CGDJkyAcffPDcc89Nnjy5TZs2ixcvjoiI0Ov13bt3D5ESAKAM/wSQn5+fnp5u\nNBr79etXUlISOoUwePDglJSUGTNmTJ8+vbCwcMGCBWyZnpiYGE0Xgtr9zv3v559/vv/++9lwnDvv\nvHPr1q1qRxRQv/32W3x8/JYtW5QtoVMgM2bMiC9n7969nlAqhNTU1KSkJPZKhwwZsnnzZrY9dErA\nV35+vjIOyRNihfD5558PGDCAvdiRI0dmZWWx7VouhEY72/fp06fdbnf79u3VDkQrqEAQMoXg8Xh+\n++23pk2btmvXrsyuECmBqoVOIXgqX6ZHm4XQaBMSIYSQ4NLYOjUQQggJUpSQCCGEaAIlJEIIIZpA\nCYkQQogmUEIihBCiCZSQCCGEaAIlJEIIIZrQ2KYOIoQpLCx85plnKtv7xBNPvPfee0OGDBk7dqzf\nL/3EE0/U7cxPPPHEXXfdNW7cOL+HREhQoIREGiePx6MsPXD+/PlDhw61b9++S5cubEtRUZHT6WQz\nb/pdnc/8zTffNFBIhAQFSkikcWrZsuWnn37Kft6/f//DDz88bty4adOmKQdkZGSUn0/FLxruzIQ0\nbpSQSIj68ssvb731VjYp8j333JOXl+d0Oi9fvjxs2LChQ4d+9dVXTqezqKiof//+48aNa9KkCXvW\nwYMH16xZI8vyjTfeOHz48J49e1Z2ZoPBcPnyZXby/Px8p9N55syZP//5z5MmTVLOtnv37s2bN+fk\n5HTr1m3ChAm+J6nwQhs3bvzhhx+GDh166623Arh48eK//vWv1q1bT5kyxfe51V6XEG1qYrFY1I6B\nkIaVn5+/evXq22+/PSkpSdk4efLkqKio/v37P/744zt27Pjyyy+bN2+elZWVnp7+v//975133gFw\n7NixjIwMALfffjuADRs2TJkyxeVytWrVatOmTatWrerXr1+nTp3KXI6dWa/XX7x48fHHH8/Kylq1\nalXTpk3/97//bd68+dSpUzzPA/joo4+effbZI0eOREVF7dy5c+XKlYWFhbfeeqter6/sQk2aNJkz\nZ47T6fzrX//arFkzs9n873//e9y4cd27d/cNoOrrEqJdqs41Tkgg7Nu3Lz4+PiUlxXfjjTfeKIpi\nQUFBfHz8n//8Z5fL5fF4fvnll169eimLVly8eHHgwIF/+9vfPB7PmTNnkpKSJkyYcPHiRY/H88cf\nfwwdOnTYsGHlL8fO7PF42Mn79OmTnZ3t8XguX75877333nbbbezpffr0eeCBB86cOePxeIqKiqZM\nmRIfHy+KYtUXSk9Pj4+Pf/HFFzds2BAfH//yyy+XD6CK6xKiZdTtmxA8+OCDHMcB6NSpU2xsbJ8+\nffr27QsgMjKye/fuFy9eBLBjx44zZ86MGjUqLy/v6NGjBQUFw4cPl2XZdzHACt17773x8fEAIiIi\neJ4/d+7c5cuXMzMzCwsLp06dytbVbt68+ezZs9nxVV/owQcfHDVq1OrVq59//vmbb75ZeVYNr+uP\n0iKkodA9JELAsgITHh5+/fXXKw+V+y4ulwvAnDlzyjw3Nzf3lltuqeLkvmdr2rQpAI/H88svvwDw\nvQXVvXv3iIiImlxo9uzZa9euvXTp0pQpU9gJa37dKuIkRHWUkAipkdjYWAALFizQ6XS+28s8LC88\n/Go7BEsJHo8nJiYGwIULF5RdRUVFV65cqcmFWNtjy5Yt33jjjbvuuqt169Y1v251r5IQNVGTHSE1\ncvPNNwM4derUn0vt37//pZdeqtvZbrzxRgBr165VtrDeE9VeaMuWLStXrhw3btzChQt//fVX6pRE\nGhOqIRFSI7fccsvgwYOTk5MvXrz45z//+cCBA8uXLx8zZkz5ZcJrIjExccCAAStXroyIiLjrrrsO\nHjy4dOnSai904sSJOXPmdOnSZdasWZGRkQ888MDatWvvvPPOUaNG+fW1EqIOqiGRxo81Xvk2YVV4\nAFNmsI7vrrfeeuvee+9dsWLFlClTbDbbfffd9/TTT9fw6r4nZ1uWLVs2fPjwTz/99LHHHktOTh43\nblxERATbVdmF5syZc/r06VdffTUyMhLA/Pnz27dvL4piXl5eza9LiGaFUbMyIbVy5cqV3Nzczp07\n++Xzvbi4OC8vr3PnzuVHrfr3QoRoHyUkQgghmkDfvAghhGgCJSRCCCGaQAmJEEKIJlBCIoQQogmU\nkAghhGgCJSRCCCGaQAmJEEKIJlBCIoQQogmUkAghhGgCJSRCCCGaQAmJEEKIJlBCIoQQogmUkAgh\nhGgCJSRCCCGaQAmJEEKIJlBCIoQQogkRagdQF7t3787MzCwpKbn11luHDBmidjiEEEL8IPhWjE1O\nTl6+fPkNN9xw5cqVY8eOjRw5ctGiRWoHRQghpL6CrMmuuLj4/fffHzly5Pr16zdt2jRu3Lgvv/zy\n+PHjasdFCCGkvoIvIZWUlPTq1Ys9jI+PB3D58mVVgyKEEOIHQXYPqXXr1sOGDbNarR07drxy5cqH\nH37Yt2/fG264Qe24CCGE1Ffw3UP69ttvp0yZ4na72cNly5YNHTpU3ZAIIYTUX5AlpPz8/CFDhiQm\nJr7wwgstWrRYunTpxo0b7Xb7gAEDyh88ceLEXbt2BT5IQgjRpqSkpI8++kjtKCoVZE1233zzjdvt\nXrRoUadOnQAsXrx4wIABDoejwoS0a9eu7OzsgMeoIQkJCSFeAqBCAECFAIAKAQCQkJCgdghVCbJO\nDaw+9+uvv/puDAsLUykcQgghfhNkCWngwIEtWrR47bXXNmzY8NNPPz333HMXL16ksbGEENIIBFmT\nXdeuXZcsWbJgwYKnn34aQGxs7IIFC5KSktSOS6OmTZumdgjqo0IAFQIAKoRgEGSdGhQnT54sKSlp\n3759Fe111GRMCCG+NP6pGGQ1JEW7du3UDoEQQog/Bdk9JEIIIY0VJSRCCCGaQAmJEEKIJlBCIoQQ\nogmUkAghhGgCJSRCCCGaEKzdvgkhpCZCYZJljU+ZWnOUkIKELEOS4HQCAMdds6tbNwDIyangWd26\nIScH3bqB47z/CAkxoTDJssanTK05SkjBQJJgMMBigV4PlMs9FWYpJicHsgxZht0OSfIewzITO5Xy\nREpXhBC1UULSPFGEzQaHAzxffqcMWfm/EnpWOQIAWQYASQJK05hc+kSWt1CamYxGCEK9QyeEkFqg\nhKRhsgyTCQBcLt/NIkQAEiQJEoCr+aYSEiR2AM/xVli9maayfMPaBu12iCIEAXp9hYmQEEL8jhKS\nVinNdGZzmT0yZA6cGWYrrFWnIt+nAGAJrBocB0GAIHgb+gwGcBzMZqowEUIaGnX71iRRhMkEh6N8\nNgJghdUMMw++htkIpbUoAUItYmB5yOWC2Qy7HTodRLEWTyeE1FJeXt5tt9122223vf/++2V2FRQU\nsF3JycmqxBYYlJA0RpZhMECW4XKp1Vamg84Ekw02oLTC5HDA4QAAne7qbSdCiF9dunRp79698+fP\nHzZsWJldLVu2fPPNN6Oion755RdVYgsMSkgaIkOGTgezGVarimE44NBDb4e9bGZigRkMVFUipOEM\nGjSoGxvLAVy6dCk3NxdARESEXq/v0KGDqqE1OEpIWiFB0kFXWW+6QGKNew44KshMPA+HA5JEOYmQ\n+khMTJwyZQr7+cqVK9dff/3y5cuVvd9++23r1q1nzZoVHR3dqVOnvn37njhxQqVIA4oSkvpkyAYY\nTLLBAfWzka+KMxMnSlYjAG8PQEKCiygiLEyFf9d+hzMajampqUVFRQC2bNly4sSJsWPHKns9Hs+F\nCxcOHDhw6NCh7du3//zzz3a7PdAFpQZKSCoTIRpg4EXJJTt48GqHUzHfzMSBM3GizmgRORvdUiLB\nx2yGx6PCv2s7KE2aNOnSpUtr164F8Omnn/7lL38p3xy3ePHiLl26DBo0aPDgwcePHw9cEamHEpJq\nWBudBMll4MzdrJqqG1WGdTd3wGHlHLJZ0Dlkk2yQZJvacRESZNq1azdmzJj//Oc/xcXF6enpkydP\nLn9Mly5d2A/NmzcvKSkJbIDqoISkAhmyCSYTTFZYHQaA54NrlA8HjgdvhdXBuTheECHqoGPDdQkh\nNTR16tT169d/8sknLVu2vPfee9UORxMoIQUay0YcOBdcvEH09l4LTkqFySHyss2ig07tiAgJGnq9\nXqfTPfXUU0ajsUmTJmqHowlBmZD++OOPNWvWJCcn79u3z+PxqB1OLciQddCZYTbD7O0UoGoPb//g\nOM5stfIuh8h7x8/SjSVCauDRRx89d+6cqVz/oPDwcOV/3y2NXvBNHZSdnT1hwoQrV6506tRp+fLl\nQ4cOXbZsmdpB1YgEyQCDAw4evPdTm402bRw4jjNbYSydcEgQYDTSDOKEVOH333+/8847e/bsWWb7\nwIEDfb9qr1u3LrBxqUb9rOt2u7dt22az2Y4ePZpT4aI+15oxY0bXrl0zMzPXrVv38ssvb968WQ6G\n7+MSJBNMV7ORJDWqbKRgLZDspRkMMJm8k4sTQnz89NNPoii+/fbbzzzzTJldy5YtK7+iYFFR0eLF\niw8ePBioANWhcg1p165dM2fO/P333wF07Nhx5cqV4eHhc+fO7d69e4XH/+9//zty5Mi///3vFi1a\nABgzZgzHcS1btgxo0LXHspEVVh48TKbGVjcqj6UloxF2u7dlkqZnJcTHxYsXd+7cabFYHnroIWVj\nTEzM1KlT8/Pz2Ueir5KSkoMHD95+++0DBgwIbKSB5VFPTk5Onz59RowYsXr16vj4+PXr12/cuPGO\nO+6YMGFCZU9ZtWrVjTfemJmZOW3atAceeOCpp546ePBgZQfHx8c3TOC1Y/VYOQ/nfcDzHkFQNZyA\nc7k8VquH5z0c57FYPC6X2gGR0KKRz4EGVfPXqPHSULPJzul0ut1uq9U6dOhQtmXo0KGzZ8/+7rvv\nCgoKKnzKyZMnS0pKnnjiidjY2GHDhn3//fdjx45lcz1VKKFUSkpKg7yG6ogQ7bC74PLOmsrzjaEX\nQ62UmZ7VYGB9xKtcVJAQ4h8pKSkJPtQOpxpqNtkdOXKkS5cucXFxvumne/fubrf78OHD/fr1K/+U\nK1eueDyef/zjH2waqNGjRw8fPnzVqlUzZsyo8BLZ2dkNFHxNmGCSITvg8C61F+LLsJa24zkkyQ67\ngbcJMm+U9Ry4oBgUTEgwmj59+vTp05WHGs9JataQevTocfz48TL1m61btwKo7B5STEwMgDvvvJM9\n7Ny5c5cuXVzXLqiqEawS4IADkuSdwzuUs5GC4zheMPMOBxzgOANnEp0GWRcGnc7bCcJmo34QJDTR\nekhqJqQRI0ZERUUJgvDFF18A+Pnnn998882UlJS77747Ojq6wqckJiYCUDrjFRYW/v7773/6058C\nFnMNSZBssHmzEVtqjyoB1/IOquVcMFsMLk508LLZCL0eTqd3+kudzpufgqEXJSH1R+shqdlk165d\nu3ffffeFF1745z//CeCdd94BMHToUPawQrfcckuvXr1effXVFi1axMXFvf322xcuXBg9enTggq4B\nNnu3Aw7YbBBFWINjnjpVsLRkhNHO2Q2cKEAwCmYOHGTZ+4/10+M48Dz0evA8jW0ijcbp06cLCwvj\n4uJ8Z2oYNGiQMtHqpUuXTp48ef3114fIekhq9rJjrly5sn///rS0tK+//lqW5WqPz8/PHz9+/E03\n3RQfH3/HHXdkZGRUdqTK/UlY1zJSYy6Py+KxcB7O4rG4PK5r97k8VqtHELxd9QipMW32Kzt16tTd\nd9/NPoT/9Kc/rV271uPxsLsPv//++/bt21u1avX88883b94cQGJi4h9//OHxeMaOHfvcc8+VPxv1\nsvObJk2a9OnT56GHHho2bJiyTmIV4uLiPv744z179mRmZm7btu2+++4LQJC1ZrPBbm/kg438TZkZ\nD4ABhmtma2Vd9axWWkmdNA52u3337t3Z2dmnTp0aOnTozJkzffd6QnU9JBWa7FauXJmZmVn1MQsW\nLIiKiqrigMjIyMjISL/G5T+hMPS1wVxtxINdB50AwQyfyWdZV71u3WAygeeDd15aohYRogWWuj3X\ng+pnzgxDWIXbLbD4vpPj4uLOnTu3YsWKMWPGpKSk/PHHH+WfwtZD6tKlS+ish6RCQsrLy8vKymI/\nnz59uqioKDw8vEOHDkVFRWfPngXQvn37wEflN5SN/KGatCQI4HnY7dDp6BYdqRXv1MYNpiZJC8DD\nDz+cn59vt9sXLVrUpk2bZ555RhTLLuBC6yEFwrPPPitJkiRJq1atatasGc/zW7Zs+eabb3bt2vXx\nxx936NDhtttuq7p6pF2UjfzKtxGv7JJLrKpkNkMUUe4vmRCNy8rK0uv1+/fvP3HixNSpU1966aW9\ne/eqHZT61LyHlJaWVlxcvGTJkuuuu45t6d+//7x5877++usKK7BaZzBQNmoISlqqYHIHdmMJdFeJ\nBJn09PSRI0f++OOP0dHRffr0AaD9OTkDQM2ElJeXFxcXV+ZWUOfOnQEcO3ZMpaDqymAAx1E2ajgc\nOCsqmnVJmV/cYKARtSRYzJgx48Ybb+zdu3dERMTUqVPnzp3bq1cvZS+th6SC7t27r169+vDhwz16\n9FA2rl+/HsANN9ygXly1x7JRqE1Spyns24DJBKeTejoQ7YuJidm0adOZM2cKCgri4uJY924FrYek\ngiFDhkRGRk6aNOndd9/95ptvNm/ePGvWrPfff1+v18fGxqoYWO1QNtII9luQJLqlRIJFTExM165d\ny2QjWg9JHV27dk1OTp43b95bb72lbDQYDK+//rqKUdWOzQY0imXIGweWk5QVmAgJKrQeUphvxVAV\nxcXF+/fvP378eKtWreLj4/3YWJeQkNDgs31Tt2MNYhMOgXISAQLzOaC2mr9GjZeGmk12Fy5cyM3N\nPXHiROfOnQcOHHjrrbe2aNEiNzc3NzfX7XarGFhNiSJ4nrKR1pg4UTbqAXirSoSQIKFmk11aWlpl\n86hu375d48NjRYhyN4vVqMWVL0IcB87EiZyZM4vgdDpocnUSQkh5aiak/v37z5kzR3lYVFS0Z88e\np9P597//vU2bNioGVi0ZsgUWF2elmac1SJniwWC2CZCNBh1npmbV0JWUlKTxVenqLykpSe0Q/EP9\ne0hlLFq06Isvvti6dWv9+903XGupAQbeJpsF+uqtaTJkO+w22SLYYYTAGc30BYKEOLqHVDv33Xff\niRMnDh8+rHYglZIgybJk5qhnndZdswag0SZKBuoRToiWaS4hHThwAEBxcbHagVRKhGiVBGoCChZX\n05Ig6IwW0aajtESINql5D2nnzp1sXgZFXl7e1q1bo6Oju3fvrlZUVbPBBknieWqsCzLe6cM5o12w\n62SLYLOZObqxRIi2qJmQDh8+/Pnnn/tuad68ee/evWfOnKnZtY5MMLlkC3hO7UBIXShpSRREnWwQ\nZMEsGyktEaIRmuvU4Ed+v31ngomzSdSXoXGQIYsQJdkmSJzZycNImYk0ftSpoVIbNmyYMGFCmY17\n9uwZO3YsW6lPUyRIkmyjvgyNBps+3MG5ZIHXmW2i0wCdDqJIy1gQohZ1muzy8vI8Hs/BgwezsrJy\nc3N9d+3ZsycrK0uDyyOKEK12HmZe7UCIP7G0JHNmk9lkM0ouUYZO513SAgDHUbWJkIBRJyGNHDmy\noKCA/WwwGMrsjYmJ0dqKsd6+DHpa7qhx4sA54JA5GVYOZrN3NjxZvlpbYmlJr6cURUjDUece0sqV\nKy9fvrxr1y6HwzF79mzfXc2aNevfv39NetmdPn16+fLlkydPVhacLcOPraU66Kw2nheovS70sLQk\ny3A6IcuQJO+c4pSWSBDS+D0kdWpI48aNA9CmTZucnJxJkybV7SRz587dvHnziBEjKktI/mKDjZNk\nnqepo0MSx3nndxAEAN6cZDKBp34QhPiZmp0aRo8evXbt2ro9d/Xq1dsDo/+qAAAaiklEQVS3b/dv\nPJWxw26EQLPOEADgOAgCHA5wHEwmmEzUCYIQf1GhhrRy5crMzMzp06fn5uampaVVeMyCBQuquI10\n7NixV155Zd68eS+++GKDhenFJgoSOOrqTXywXg9GI+x2GAwQBBiN9JWFkHpSISHl5eVlZWWdP3/+\n5MmTWVlZFR5z5cqVyp7udrtnzZo1atSoO+64o8FivEqEaJYFGglLFCJEM0r74JVJS7QkICH1EHwD\nY5cvX56env7ll1+ePXtWr9evXLmyX79+FR5Z/9t3MmQddB7ZRV9+icIEkwRJgOBNSwzrmGezUVoi\nWkadGspiTXZVH1NZk90PP/zwzjvv2O32yMjImgyeVdZBmTZt2vTp02sbqgjRItPdI3INK6xsYQsd\ndAIEI4wcuKu1JVGETgez2dsJghBVpaSkLFu2TO0oakqFGtIbb7yxbt26qo/5/PPP27ZtW377M888\nI0nS7bffDqCoqGjHjh2JiYkjRowQKvrjr/93gTCEuWQHx/H1OQlprLzrLcF2NS0xrBseQL3DidZo\nvIYUZE12aWlpP//8M/v53Llz6enp995779133z1y5MjyB9ez6E0wAbCCxh6RqihpiQdvhtmblpQW\nPJ6HmRYGJFpBCal6brc7Ly+vRYsW7du3r/mz8vPzG/Yekk5H33BJDbG0ZIGF1ZZ48ADdWCKao/GE\npPICffn5+bNnz+7Tp8/dd999xx13JCUlLV++vIoudr7CwsIaMDKbjSaJITXHFrZwwcWBM8Gkg84G\nm/fGksMBwDtzKyGkcmrWkC5cuPC3v/3t8OHDgwYNuuWWWy5duiRJUk5OzsSJE+fOnVv/89fruwBV\nj0hdyZAlSHbYZchXO+PJMgwG77RD1IJHVKLxGhI86lm9enV8fPznn3+ubHG73c8880yvXr0uXLhQ\n//PHx8fX8ZlWq4fn6x8ACXEOj4P38JyHs3gsHo/H43J5LBYPx3ksFrVDIyGq7p+KAaFmk92BAwfi\n4uJGjx6tbAkPD580aVJJSYnKOdxupxZ/Un88eAccDjjYgDaRs1MLHiFVUDMhxcbGXr58uczGwsJC\nAGouYW6zAaDGOuIv3pUAUbp2iXJjSZKg00GS1AyOEC1RMyHp9fqCgoJ3333XU3of69y5c8nJyTfc\ncEN8fLxqYVH1iDQA1uvB5zEHhwNmM0wm6HTer0GEhDY1OzVs27Zt3rx5ubm5HMf17t27sLBwx44d\n58+fT0xM7Nq1Kztm6tSpPXv2rNv563L7jg1pdNFUqiRQ2HoWbD1Amt+BNDCNd2pQZz0kJi8vr7Cw\nMDY29uzZs9u2bQPQtGnT2NjYnJycnJwcdszYsWMDGpMoUvWIBBRbz0IQYLPBboco0qAlErI0MTC2\ngdT6u4AkwWBA4y0QEgRk2bvGEqUl0gA0XkNSeWCsttjt1GBCVMZxOocsOnhZskGno9X/SEhRs8nO\n7XYvXrx48+bN586dK7MrIyOjwslVG5YkebvkEqIeBxx2zm5ycJxNMooGnqOqEgkVaiaktWvXrlix\nonPnzgMGDGjSpInvrqZNmwY4GEm2GVyyR5mwmRCVsP54RhglQRIFu0m2CMA1s4kT0kipmZC+//77\n2NjYdevWqTnqqJSdcwoQ1I6CEC8OnABBgCBzsgjRAAMP/uq0rYQ0RmreQ4qOjg4LC2vWrJmKMSgk\nSNcMEyFEG5RxtXpREiWDDjoRogxZ7bgI8T81E5LBYDh79uxnn31WUlKiYhgAbLBx4KhJhGgWB04w\nOhxOi0MnyzaLAQYTTDbY1I6LEH9Sudv3s88+m5GRERcX161bN9/ty5Yti46OrufJa97B0QSTHnpq\nsiNBQJIgirIsSTzsRsg8J0DQQ09NeaQmqNt3pbZs2ZKRkdG8efPo6Ojz1wpwmmTLfQbyioTUEc/D\n4eBcHsHscshWh8hDkgww6KCzSSbqJk6CmpqdGnbs2NG6deuMjIyOHTuqGIYNNgECtdeRIMNxEAQO\nghkwQpYgOXmnoDPQiFoSvNRMSG3atGnbtq262QiAHXYjjOrGQEh9KF3y4JBhMACgnESCkcqdGnJz\nc9ksdiqSIAkyr24MhPgHm0QctN4SCUpq1pCuXLkSHx8/depUg8HQoUMH313PPfdc69atAxADa6+j\nJaVJ48HWWzIaqapEgo6aCemnn346cuRIRETE1q1by+yaNm1aYBIStdeRxolVlex26HSwWn0XnDTA\nYISR+pQSDdLcbN/79+9PTU2dNWtWYLp9hyHMA22VACH+xKYP5zgYjSwt2WBzwilB4sHTaIdQo/Fu\n31pJSGfOnFmzZk1qauqhQ4cAbN++vX379vU8Z7VFz0a8W2Gt54UI0TTfNQB5Hno9BEGGLEGywy5D\npswUOighVcXj8WRmZqampm7evLm4uDgiIkKv148aNeqee+4pM92qr127du3Zs8ftdvfv33/AgAGV\nHVZt0eugs8pmnhPq8xIICRosMzmdsNm8CwPq9TLPSZBYnYkDR615jRslpIr99ttvaWlpaWlpv/zy\nC9syYMCA5OTkmJiYqp/42muvWa3Wnj17XrlyxeVyjR8/fv78+RUeWXXRS5AMMFB7HQlFsgxZ9taZ\nZBkOBziO1ZmccLKZtGgCiEZJ4wlJhW7fe/bsefzxxw0GQ3JyclhY2JNPPrlhw4Zu3bpdd9111Waj\ngwcP2my2qVOnrlu3bv369RMnTvzPf/6jrHdeK3bY6ZsgCVEcB56H1QqHA2YzTCaIIktCVlhdcJlh\nliGzCSBEiBIktSMmIUGFhJSeni5JUp8+fT799NPNmzc//fTTOp2uhs89cOCAx+N59NFHAYSFhd1/\n//0Ajh49WocwJEhmmfrXkZAnCLBaASgL1PpmJnaHVYSog84Ek6qBksZPhYTUo0ePZs2a7d27d/78\n+R988MFvv/1W8+cOHDhwxYoVSkVq3759ALp06VLbGGywcZLMcXxtn0hII8SGLpVWla5uBseDN8Ps\ngMMBB02vRRqaOveQzp49y/rUHTx4MDw8fMCAAd9///2wYcNee+21mp9k48aNzz///KBBg955550K\nD0hISFB+njZt2vTp05WHJpj0EgSe+tcR4oPdWLLZ2F0ltaMhfpCSkrJs2TLfLVq+h6RyL7usrKzU\n1NSMjIwLFy5ERkbed999Dz74YP/+/cPCwqp4VkFBwSuvvJKenj58+PCFCxdWtuBsFbfvwhDmkh1U\nQyKkAjYb7HbwPM3y0PhovFODJsYhFRYWfvXVV6mpqawJrlOnTl988UVUVFSFB//000+PPfYYgDlz\n5owYMaKK01Za9DYbTCZo4IUTolGsqmSxeLs/lA6qrQkddDSwSbMoIdXCkSNHVq9e/cUXX6xdu7bC\ngbHnz58fPny4Tqd7++23q53KodKiN5kAeG/kEkIqwzqFO52QJMiyNznp9VUnJxmyHXZJtskcAAgQ\n9HI3XgJQ2ruPqIcSUq253e7KRsXabLZXX3110aJF7dq1UzYmJia2atWq/MGVFn1YGFwuaiInpBaU\nQbVsDcAqVgJkuzhO5iDxcBo5mYMsS5wMox2cDF7mwHHeP0C9nrJUIFFC8ie25HmZjZ9++mnfvn3L\nH1xx0YsiZJmqR4TUXdXr0lb0VU8ZdStDlmWJlzm9zHFOmQfvTXVcaZaiFNWQKCGppuKip+oRIaq6\nJjmVzqTHyfA267FKGEtRrGMF/bX6DyUk1VRQ9HT3iBAtKZOcXHCV7pABePug06Ls/kMJSTVli16W\nodNR9YgQbZIhVzD2VhkaRWnJHzSekNRcwjzQRNHbjZUQoj0VzwTBZpFwOCBJ0OkgSQGOigSSmivG\nBpQkwWajsUeEBCW2AK7NBpMpzCWzOY300LMf1A6O+E3INNkZDDAaIQhqBkQIqSdZliU77DaJk516\nyBxkngNA+amGNN5kFxoJSZJgMsHlqu4ZhJBgwEbsyjJycrw/yRLlp5qghKSaq0VP1SNCGj2WmCDL\ndlGG7DRybDFcat/zRQlJNd6iL60e6aCjKfQJafzYMCa7HbIsmwUIRqVnuQOOio8vP9RX2dK4RulS\nQlKNt+gNBpjNNl42wUQLlhMSQti05bIMs/maBhKWsXJyIElXJ4koQ9lis12dxC/4kxMlJNUkJCRk\nv/AC7HY4HCJEGTJb/pIQEkKUtMRfO01RJRPFhiGsdBYjztvQJ3PeefyCPzlRQlJNQkJCdnExrFbw\nvA46M8w0Hz4hIcp3OqKqD4QMQIIEgDX0sRtR3hQlc5AkzinzttLpz2uzNofqKCGp5v9dd91rvXrB\n4QAQhjBqryOE1E1prz5ZmeUIAGSZk8HJ0OdwXDcevJ6XtV5tooSkml+bNeu0cSN43gabE05qryOE\n+Ev5ipQM2RWm9WoTJSSVSFL6qFGjz54FYIKJ1q8khAQCm3yPLWnI7jZdO+DEBhuA2vVBV7r8KYtR\n5eQAqHaxxPIoIalGKXrq8E0ICTSf3udXM5Msm2SDt3bFe6fv4ySZk8HloJsMrvTfNZQuf+wH5SFr\nR6xNJwtKSKphRW+DjTp8E0JUoyy2yzrp4erYJlnfDRwncTIAJ5zwuVnlzVWlnSm6oVvFlaoyJ6+u\ntZASkmpY0ZtgAkA3kAghQUTJTPDJVRUP7PU+QQZwtbWwktU6KCGphhW9DjorrDRlCCEkFPz/9u49\nKKqyjwP4D1h0VmTlNiMl0DroWRmIJYIFJ24xcksTy0x2MnVo1aEpkzJ4R0aDdDDKC4SDQ4WXbESU\nZJQoB5HcagaUCgZDBZPbGIuJQCjLyrKc94/ztu8OKFKRe3bP9/OHs+fZ3fM8+x3wxznnOfvYkA13\nxk9KUqk0iogiKZL+XN1jg2wDCpJ5cAUJE74BQDi4g6r//Xv+cIf6UPsTRGvXci1dsi4+FyQrXw/p\nEB3C5DoAEA7jxScioqgokr5HWVmUdZ47iScjmVlH9xBWvmKsmtTc4SoAgBBJpXTwIH37LbW309y5\nc/R6cw9oIhZ5yq6+vr6mpkav1wcEBERGPrDeyGSy4eZhTPgGAODwfFKD5R0hHThwICkp6eTJk2fP\nnl2/fv3WrVsf9MqBFwaMsyeFKT8/39xDMD+EQAiBiBCCJbCwgnTnzp29e/cuWrSosrLyq6++UqlU\nx48fv3r16n1frA3RCvwC0r59+8w9BPNDCIQQiAghWAILK0h1dXXDw8PJycm2trZE9NprrxHRDz/8\ncN8XDymG1tCaRzo+AAD4uyysIHV1dRHRvHnzuE0XFxcXFxeucTz9HD1uPwIAsBQWVpDu3btHRGKx\n2NgiFot1Ot34V56n85IyyaMbGQAA/DMWeR+SwWAwfcydvhsjiqKWnlwq+w+vJ90/AjKZ0BMghEBE\nCIGIEAKRQqEw9xAmYmEFadasWUR069YtLy8vIjIYDL29vRLJ/Y+Ejhw58kgHBwAA/4CFnbLjrh5d\nuHCB22xoaBgeHjZeUgIAAMtlYUdI/v7+Uqk0NzdXIpE4ODjk5ORIJJLo6GhzjwsAAP4py/umhubm\n5nfeeefatWtENHv27Ozs7LCwMHMPCgAA/inLK0icvr4+g8Hg5uZm7oEAAMDUsNSCBAAAVsbCJjUA\nAIC1QkECAABesLBZdpM0yfUprNWePXuef/75+fPnG1sEFcjFixd//PFHg8EQHBwcGhpqbBdOCGq1\nuqGhwc7OTqFQmN4IKZwEjPr6+goKCpKTkx977DGuRTghdHR0XLx40bhpa2u7fPly7jFvQ7DLzMw0\n9xim2IEDB1JTU7u6ujo7Ow8fPvz7778Lal54WVlZTk5OeHj43LlzuRZBBfLBBx9s27att7e3ubn5\n4MGDfX193O+bcELIzMzcuXPn4OBgTU3N0aNHR0dHuaosnARMbd68uaysLC4ujitIggrh6NGju3fv\nbmpqqqurq6urq6+vf/XVV4nnIbDWZWBgwM/P7/XXXzcYDCzLfvjhhwzDXLlyxdzjehRyc3NfeOEF\nhmEYhjl37hzXKKhAmpubZTLZrl27WJYdHR3dvn07wzDt7e3CCeHXX39lGObTTz9lWXZkZGT16tVP\nPvnkyMiIcBIwdeLECblczjDMTz/9xArsd4Fl2Y0bN27cuHFMI89DsLZrSH9pfQorM2vWrJCQkJUr\nV5o2CiqQpqYmlmW5z2hjY7N48WIiam1tFU4ILS0tYrE4KSmJiOzs7MLCwvR6/eDgoHASMOrs7MzO\nzjZdwFNoITQ3Ny9YsICIWJOp1DwPwdquIf2l9SmszNq1a4mou7u7pKTE2CioQBYuXFhUVOTk5MRt\n1tfXE5Gnp2dtbS0JI4SEhISEhAQi6u3tbWpq+uKLL6KioiQSiaB+DIjIYDCkpaUlJiY+88wzxkZB\nhaDT6To7O6urqw8ePDg8PCyXy7ds2SKTyXgegrUVpMmvTyEQggrE3d3d3d2de1xZWZmXlxcdHT1v\n3jy1Wk2CCYHz4osvajQae3v7lJQUEtiPAREVFhbevn07LS3tjz/+MDYKKoTr168bDIZp06Zt3bp1\nYGCAm9nxzTff8DwEaytInMmsTyEoggpkYGAgOzubu5Sdk5NjbBdUCF9++eXt27f37NmTlJR06tQp\nrlEgCVy6dGn//v2HDx8Wi8WmBYkjkBCkUunx48d9fX1FIhERyWSyV1555dy5c9yzvA3B2grSX1qf\nQgiEFsiVK1fWrVtHRLm5udzJKxJSCHV1dX19fbGxsa6urq6urh999FFwcPD58+ednZ1JGAkQUVFR\nkZ2dXWFhIRFxf/vn5OQkJCQI58eAiBwcHORyuXEzMDDQ1ta2o6PDw8ODeBwCXwrjVMH6FGMIKpC7\nd++qVCqpVFpRUWGsRiSkENRq9aZNm3p6erjNkZERIrKxsRFOAkQUHh6+YsUKLy8vLy8vbra3u7u7\ni4uLoEIoKChQKBTcCToiam1tHR0dnT17Ns9DsLYjJKxPMYagAiktLe3p6UlPT//ll1+MjQEBAcIJ\nITo6+rPPPtu8eXNycrKjo+Pu3btFIlFkZKS3t7dAEiAi4+2fRNTd3V1WVrZmzZrAwMDR0VHhhBAW\nFpafn5+amvrmm28ODQ3t2LFDIpHExcU5OTnxOgRzzzufelevXl28eDF3O054ePj3339v7hE9Ujdv\n3mQYprq62tginEBSU1OZcX7++WdWSCGcOHFCoVBwnzQ6OrqqqoprF04Cprq7u433IbECC+HkyZOh\noaHch126dGljYyPXzucQrPbbvrE+xRgIhAQTAsuyN2/etLe3d3V1HfOUQBKYmHBCYFlWo9E4Ojo6\nOjqOeYqfIVhtQQIAAMtibZMaAADAQqEgAQAAL6AgAQAAL6AgAQAAL6AgAQAAL6AgAQAAL6AgAQAA\nL1jbVwcBcIaGhjZt2vSgZ1NSUgoLC6Ojo1esWDHlXaekpPy9PaekpERERCiVyikfEoBFQEEC68Sy\nrHHpgbt37167ds3Nzc3T05Nr0el0arWa++bNKfe39/zdd9/9S0MCsAgoSGCdZsyYcezYMe5xQ0PD\nypUrlUrlG2+8YXxBRUXF+O9TmRL/3p4BrBsKEgjU6dOn/f39uS9FXrRokUajUavVer0+NjY2Jibm\n66+/VqvVOp0uODhYqVTa2dlx72ppaTl16lR7e7uPj09cXNz8+fMftOdnn31Wr9dzO+/u7lar1f39\n/UFBQatXrzbura6urqqqqqOj44knnli1apXpTu7bUWVl5aVLl2JiYvz9/YlIq9V+8sknM2fOVKlU\npu99aL8A/GSXmZlp7jEA/Lu6u7tLS0tDQkIUCoWxMTk5WSKRBAcHb9iwoba29vTp09OnT29sbCwr\nK7t8+fL+/fuJqLOzs6KigohCQkKI6MyZMyqVqq2tzcHB4ezZsyUlJYGBgXPmzBnTHbfnyMhIrVa7\nYcOGxsbGkpISe3v7y5cvV1VV9fb2RkVFEdGRI0fefvvt69evSySSCxcuFBcXDw0N+fv7R0ZGPqgj\nOzu7jIwMtVr90ksvTZs27b333vv888+VSqW3t7fpACbuF4C/zPpd4wCPQn19PcMw+fn5po0+Pj5Z\nWVkDAwMMwwQFBbW1tbEse+PGjQULFhgXrdBqtQsXLnz55ZdZlu3v71coFKtWrdJqtSzL3rp1KyYm\nJjY2dnx33J5ZluV2LpfLm5ubWZbV6/Xx8fFPP/0093a5XL5kyZL+/n6WZXU6nUqlYhgmKytr4o7K\nysoYhtmyZcuZM2cYhtm+ffv4AUzQLwCfYdo3AC1btkwqlRLRnDlznJ2d5XL5U089RURisdjb21ur\n1RJRbW1tf39/YmKiRqNpbW0dGBiIi4trb283XQzwvuLj4xmGISKRSBQVFXXnzh29Xl9TUzM0NLR+\n/XpuXe3p06enp6dzr5+4o2XLliUmJpaWlr777ru+vr7Gd02y36lIC+DfgmtIAMRVBY6tre3jjz9u\n3DRed2lrayOijIyMMe/t6ury8/ObYOeme7O3tycilmVv3LhBRKaXoLy9vUUi0WQ6Sk9PLy8vv3fv\nnkql4nY4+X4nGCeA2aEgAUyKs7MzEe3YsWPu3Lmm7WM2x7O1/f95CK4ksCzr5ORERIODg8andDrd\nyMjIZDrizj3OmDFj165dERERM2fOnHy/D/uUAOaEU3YAk+Lr60tEvb29QX9qaGh4//33/97efHx8\niKi8vNzYws2eeGhH1dXVxcXFSqUyJyfnt99+w6QksCY4QgKYFD8/v7CwsLy8PK1WGxQU1NTUVFBQ\nsHz58vHLhE9GQEBAaGhocXGxSCSKiIhoaWn5+OOPH9pRT09PRkaGp6dnWlqaWCxesmRJeXl5eHh4\nYmLilH5WAPPAERJYP+7klekprPu+gDPmZh3Tp/bu3RsfH19UVKRSqQ4dOvTcc8+99dZbk+zddOdc\ny759++Li4o4dO7Zu3bq8vDylUikSibinHtRRRkZGX1/fzp07xWIxEW3bts3NzS0rK0uj0Uy+XwDe\nssFpZYC/ZGRkpKury8PDY0r+fx8eHtZoNB4eHuPvWp3ajgD4DwUJAAB4AX95AQAAL6AgAQAAL6Ag\nAQAAL6AgAQAAL6AgAQAAL6AgAQAAL6AgAQAAL6AgAQAAL6AgAQAAL6AgAQAAL6AgAQAAL6AgAQAA\nL/wXz+NdXmJw4gsAAAAASUVORK5CYII=\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "% Signal Smoothing by Averaging\n", "clf;\n", "R = 51;\n", "d = 0.8*(rand(R,1) - 0.5); % Generate random noise\n", "m = 0:R-1;\n", "s = 2*m.*(0.9.^m); % Generate uncorrupted signal\n", "x = s + d'; % Generate noise corrupted signal\n", "subplot(2,1,1);\n", "plot(m,d','r-',m,s,'g--',m,x,'b-.');\n", "xlabel('Time index n');ylabel('Amplitude');\n", "legend('d[n] ','s[n] ','x[n] ');\n", "x1 = [0 0 x];x2 = [0 x 0];x3 = [x 0 0];\n", "y = (x1 + x2 + x3)/3;\n", "subplot(2,1,2);\n", "plot(m,y(2:R+1),'r-',m,s,'g--');\n", "legend( 'y[n] ','s[n] ');\n", "xlabel('Time index n');ylabel('Amplitude');" ] }, { "cell_type": "raw", "metadata": { "raw_mimetype": "text/restructuredtext" }, "source": [ ".. note:: 问题:\n", "\n", " 1. 信号x1,x2,x3和原始信号x之间的关系是什么?\n", " \n", " 2. 函数 ``lenged`` 的作用是什么?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 从声音文件中读取信号并显示波形\n", "\n" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "image/png": 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bM2N8BnxuEVgkVriQsCnnUEJF2lhNJrAtjL3jjjs++uijr7/+2uFwXH/99UlJ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\nqKgoVQoGAAoAggQAAAAQwf8Hsu4xzoSdoNEAAAAASUVORK5CYII=\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "% load the speech signal and then plot\n", "clf;\n", "[y,fs] = audioread('malevoice.wav');\n", "t = [0:length(y)-1]/fs;\n", "plot(t,y)\n", "xlabel('Time (s)')\n", "ylabel('Amplitude')\n", "axis tight" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "% Using sound function to play audio\n", "sound(y,fs);" ] } ], "metadata": { "celltoolbar": "Raw Cell Format", "hide_input": false, "kernelspec": { "display_name": "Matlab", "language": "matlab", "name": "matlab" }, "language_info": { "codemirror_mode": "octave", "file_extension": ".m", "help_links": [ { "text": "MetaKernel Magics", "url": "https://metakernel.readthedocs.io/en/latest/source/README.html" } ], "mimetype": "text/x-octave", "name": "matlab", "version": "0.16.7" } }, "nbformat": 4, "nbformat_minor": 2 }