{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Lab 5 Part 2: Digital Communication with Audio Frequency Shift Keying (AFSK)\n", "\n", "In this part of the lab we are going to experiment with digital modulation and communication. Network Communication systems have layered architecture:\n", "\n", "
\n", "
\"Layers\"/
\n", "
\n", "\n", "The bottom layer is the physical which implements the modulation. Here we will use [AFSK](http://en.wikipedia.org/wiki/Frequency-shift_keying), which is a form of BFSK in the audio range (hence the 'A'). We will write a modulator/demodulator for AFSK. In the next part of lab we will leverage [AX.25](http://www.tapr.org/pub_ax25.html), which is an amateur-radio data-link (layer 2) layer protocol. [AX.25](http://www.tapr.org/pub_ax25.html) is a packet based protocol that will help us transmit data using packets. It implements basic synchronization, addressing, data encapsulation and some error detection. In the Ham world, an implementation of AFSK and [AX.25](http://www.tapr.org/pub_ax25.html) together is also called a [TNC ( Terminal Node Controller )](http://en.wikipedia.org/wiki/Terminal_node_controller). In the past TNC's were separate boxes that hams used to attach to their radios to communicate with packet-based-communication. Today, it is easy to implement TNC's in software using the computer's soundcard.... as you will see here! \n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Using matplotlib backend: MacOSX\n", "Populating the interactive namespace from numpy and matplotlib\n" ] } ], "source": [ "%pylab\n", "# Import functions and libraries\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import bitarray, time, urllib, ssl\n", "from scipy import signal, integrate\n", "from fractions import gcd\n", "\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# function to compute least common multipler\n", "def lcm(numbers):\n", " return reduce(lambda x, y: (x*y)/gcd(x,y), numbers, 1)\n", "\n", "# function to compute average power spectrum\n", "def avgPS( x, N=256, fs=1):\n", " M = floor(len(x)/N)\n", " x_ = reshape(x[:M*N],(M,N)) * np.hamming(N)[None,:]\n", " X = np.fft.fftshift(np.fft.fft(x_,axis=1),axes=1)\n", " return r_[-N/2.0:N/2.0]/N*fs, mean(abs(X)**2,axis=0)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## AFSK1200, or Bell 202 modem\n", "\n", "AFSK1200 encodes digital binary data at a data-rate of 1200b/s. It uses the frequencies 1200Hz and 2200Hz ( center frequency of $1700$Hz $\\pm 500$ Hz) to encode the '0's and '1's (also known as space and mark) bits.\n", "\n", "| \"Space\" | \"Mark\" |\n", "|---------|---------|\n", "| 0 | 1 |\n", "| 2200 Hz | 1200 Hz |\n", "\n", "Even though it has a relatively low bit-rate it is still the dominant standard for amateur packet radio over VHF. It is a common physical layer for the AX.25 packet protocol and hence a physical layer for the Automatic Packet Reporting System (APRS), which we will describe later. \n", "\n", "The exact analytic frequency spectrum of a general FSK signal is difficult to obtain. But, when the mark and space frequency difference $\\Delta f$ is much larger than the bit-rate, $B$, then the bandwidth of FSK is approximately $2\\Delta f + B$. This is not exactly the case for AFSK1200 where the spacing between the frequencies is 1000Hz and the bit-rate is 1200 baud.\n", "\n", "
\"AFSK\"
\n", "
Figure 1: Approximate spectrum of AFSK
\n", "\n", "Note, that for the (poor) choice of 1200/2200Hz for frequencies, a synchronous phase (starting each bit with the same phase) is not going to be continuous. For the Bandwidth to be narrow, it is important that the phase in the modulated signal is continuous. For this reason, AFSK1200 has to be generated in the following way:\n", "$$ s(t) = cos\\left(2\\pi f_c t - 2\\pi \\Delta f \\int_{\\infty}^t m(\\tau)d\\tau \\right),$$\n", "where $m(t)$ has the value =1 for a duration of a mark bit, and a value =-1 for a duration of a space bit. Such $m(t)$ signal is called an Non-Return-to-Zero (NRZ) signal in the digital communication jargon. For futher optional reading, Here's a link to some [notes](http://www.dip.ee.uct.ac.za/~nicolls/lectures/eee482f/13_fsk_2up.pdf) provided by Fred Nicolls from the university of Cape Town.\n", "\n", "The integration guarentees that the phase is continuous. In addition, the instantaneous frequency of $s(t)$ is the derivative of its phase, $2\\pi f_c - 2\\pi \\Delta f m(t)$, which is exactly what we need. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Taks 1\n", "\n", "* Write a function `sig = afsk1200(bits,fs)` the function will take a bitarray (bitarray is a python module for storing array of bits) of bits, and a sampling rate. It will output an AFSK1200 modulated signal of them, sampled at fs [Hz]. Make sure the frequencies match the bits in the way described by the table above.\n", "\n", "* Note, that when `fs` does not divide by 1200, each \"bit\" will have non-integer length in samples. If you are not careful, this would lead to deviation from the right rate over time. To make sure that you produce signals that have the right rate over time generate the signal first at a rate of `lcm((1200,fs))`, the least common multiplier (which does divide by 1200) for the entire bit sequence and then downsample by the right value at the end. You don't necessarily need to low-pass filter, since the signal is narrow banded anyways. \n", "\n", "* For integration, use the function `integrate.cumtrapz`, which implements the trapezoid method. (don't forget multiplying with the $d\\tau$)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def afsk1200(bits, fs = 48000):\n", " # the function will take a bitarray of bits and will output an AFSK1200 modulated signal of them, sampled at 44100Hz\n", " # Inputs:\n", " # bits - bitarray of bits\n", " # fs - sampling rate\n", " # Outputs:\n", " # sig - returns afsk1200 modulated signal\n", " \n", " \n", " # your code below:\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " return sig\n", " \n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To help you debug, we generated a sequence of 4096 bits and its AFSK1200 encoded signals at sampling-rates of 8192Hz, 11025Hz, 44100Hz and 48000Hz. Compare the result of your function and make sure they match well. We assume that the phase at n=0 is zero (i.e., cosine modulation). Use the following code to load the data: " ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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MoaGl89R2qp3DhuOQdxy0dATpNGnrah0X0BO1aBIcjdmxIeW2z9JQrkagRjOV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\nQtoAat9NFnfnHr/6e/r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Rdy2iWxmG2Lzh33JurFJgK9/lJydgIjjRnMNyjrbQsC7nhjpP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auXAtQezIOKx0DttKi7eIVAT94SCIPaOOrufvWrUEqhzdR6QjBzHap4wvY2nD3bSXDCNENn\nSPfAFMOpHwO1dfeeVXjyrpkjBKUhEEXD2lp3GJ9VNXZRUGrkhUEHahvl8M9/OX3itZvx5FMxaPik\ntWaa7Pf7uPmDY/OXu7uAW9FLUi2jqEBKYttmq+mzV90CYFqVuCtm8G200AqLvf1n0+SpUDi53kFD\np9Yti8YZcPPRswd9R4sF/qcg5/rE9xRFzIIeH93cfebf3JjvsjWZYZ85/8Lvx1/mgx64ke7vQ+nO\nCHM9Ka63PGQGlSTod2ZR1ycPsAo9kAzLQ6awjsmPSwRmpnloPTA5So9A7W4W00sybaY2MS3s6kgC\nuXDvdxJuTSAfGAaG9LCD5JCprZVJaGoeOIVg4bmUy3xQKSHYnNNLM6yeXhRF6rhYdbdWPnwIqfOQ\nMC9wByefIz7IBzXL9gjULmf9w3W9jEbfsqiX86CHoLZaIEq9PTGQkkUU8KUfnx9im0Vd4y8S/N7J\n94VO1WDi1zlp2im2wvXONElnbYfue5aGgxdlR6DWBKE7l7xshlTL8YS33oLtSc0wTvCHGrm3S9dd\nu0i4cQNee20p4U4SbYbQBgzkkUlbWmOVFbWGhBsgME0Sx6Wt51hWJ2fOm1K7GfJJa50W6RqUz+k/\nVJakpzFy8f3lSUnpuNjscP1GzcWLkLXdfLjOeQy6Z8+WIVOzc3BuGqhtga05PnDSGi5TGFT+7Fik\nu8lyJnmo2Wxc7h2bRnAIavfiil6SIPua5Iyp8FL5VKZ2YTSYz3sgPqsf+nopQG0gJalpY6b5Iagd\npzVukWpJcAAiqWike5h19faPNbTSoEbv4OcKwcz3aJIpDx92oHZR5F2guUa30luaYjh1cgzU+lmK\n1ABThmHgti1tIzBNiCK4t19jFzmNrQlqlWLuufzGr8+eeG03nrI+nWofUk9a66bJKOqxpR4eMjWw\nlFnbNb25PlMbmyYbTcR+cxOAuM7w82KleaFNKVGqz8508sy/SS0Xvz49w4MN36d0Iu6Nnw1qk7ok\nLE5v09iwbR4N+uzeuffMv3lYbrM5nkCoB2BOUgdMrZF3J+DRCAozJtKcLz2cB60FXi9hPoeb0wfY\nmnJm6MxqiqXJ0cWLcOtBTC0dVK7Hrh+Y1dTxgvEYLl2CUZF0vyldptY0caryUH6ceg/prfCeQyGo\nlYvtd0ztNI9pDYHv6cnk3WWkWjU/WtuklyKKGtc/+bbpLedB7ao76D96BPviAf2i1voMDcPAA0Sj\nDsci9pMaq1ww1HCkhg7U5qZNWiwOQe2iLRCV3jEhkJKi73EmOLL1HhUJUbzg1Qs/duLreUKQmCb+\n8rkZj8EbNti53owudLPThrRxwvRwprY2DaTmTI4nBPPAp551nZW33oJH84q1OCFa02NqM9vGqVJu\n3uxAbbzCXDKAD2BYRyqJrMauSmpNY7pQSvZDn2I2ZjDovpecGqN+sfvEljQRjs148mw1U+E5rNur\nq4pcIcgtizB1+eZHt7l8uVO62bmeGzocqQRiuwO1o1l3BjU1Yip1aqAU0zDCLa8904jxQZawNp1q\nkxMH8vmtxmOUdmZRk6KmH6fIvoY3jBAslCTIxFOZ2sQS2I3mPN1n9UNRLw2oTZR5DNROsgY/S7Wc\n5wCC5TxosQwcT5sGs6zIlZ4U15OSuedCOj1kapOyRGoEmh9crzsEpYegNqUmWiwwNdyUATyjpaFz\nwdrY6ECtk2XUmiYAgZRMfI97H4+PAUmAebbAT9IXKj96vNaUYuwFnFF7hyZYsAS1ZtPJjzU27UBK\nYqXYaDymxi0A4jbr5oVWcFTcsCwMe8hu+uiZf5O6AVH74oHa4T1FEYkb8mj2bFBbSMHglL5TgA3T\nZGc4IH34bFCbsEuUxqfyrHlSklk2aulGOhpBKlNCTdZSLJkTCxs3SoljuJc+wslXA7WZaZGmcy5e\nhNvT26B8VKmX49sdTDzyScIXvtB5TU2rgt5spm0UtZAKp+okkFUFVbTbgVpdplZKatPB9LqZ2u/t\nfYwqC4SmmZsnJQvfo46PQG1lFIhcaH0tB+u5X2WUZccyPiofMUwK7ffsGwamcEnynLaFB9kjnDzV\nykuG5Qxs4FHOJmxudmtnYYCq9PbEg9QC4qMZt9vzh6wlBbJ3coAXSslCmvhLhn8yAa9fIxczzp/7\nnNY9ekphGBa2n3bRNm1LbQpMXVC7jOI7eG7eegvGaTeX7IUn//11+fI2TpkdMrVxXRMkibZSKBAG\nrbAfM2lrcIqcRsOFG5bngNCnnI0ZDjtQWxgNon6xMtot26axhny8vfPMv8k9nzPR6mZVnpSkShHN\ne/zu7Q957bXuzOhmGZaGZ8rBNQ1hknpXAbi/X2PmpZYbuk6tmSbjIGTNunPMrOrx2ikyNiergdqF\n67LROIdMbVw19Bcx5kAzGk0I3Kx9KlOb2CaOPJ3P77P6wayXAtT6UpIKiZmVhzmEk7zGz1OEZpcs\nUIrGdEmrjipY1DVOXZJr/iAO8iuNdMb2Npw5A0lV6bsoCkFm2tjlY6DWqOktFtqznJ5h0Bhd13Jj\nAx6Ma5w8o9XIWYPuPU8Dl598Z8Kv/urx19I6x8tPj9HzpaSWgr6aPwFqa1l3+b4aMqRASmIpWWsd\nYnkLgAUFYZprS94A1l2Xyu4zLp4Oatu2JXFDBtbpuAxD5yC9H/VI4vvP7NyWtsUZ58VHDB3ek2my\nN+hjTB9QP0OVVVpz+sWTEvgXUf6yY6+Wwe/7+5AZGdEi0QehhoEUDm6UEMfwKN/HWwHU+lIyC33K\neEavB/RuU5kOqtJkapfd9mae8s470LQNSVXjp5mWEYwvJQspccqKJOkiReRwRE9zLhm69bxSHaid\nTOCD0XWsokSE+p/hPPBplkxt1VQUTctW3+TCBY3rLZnaoMrY2emsBu6n+91csqbC4ADU2n43Y3qr\nvIqfVRgrmLpMQ59yOmJjo2NqSyExK03wtAS18jFQe2+xzyDWe8/BUqrvldWh/FgOp9hJzMVXTs78\nwlEEj+11oDZtGkRdEw31XPsP5JbtUsv81lswrzJUkWlFBfpSHubK3rgBly93Z5VgoWe2BRBIRSuP\nmNpZUeOWOa1GkkR3Pck08ClnEwaDpXpFgNCcS/6kte44lM6Aa4+e7oCcJC2JH/DK2up7qC8EiRIE\nccD7Ox9w+TLMq5Igy5Aa2dqwdFg3FEXYMbX3JzV2XmhFNelUJCWlUqy5u8/09hjVOWfGE235sScE\nC8dhrbYPmdqsbRnGMdbw5M08UwgUYDVPZ2oXrsvAP/k4x2f1w1MvBai1DIPWMBBFezRTm9cEWYrS\nlOCElkVheWRldxhOmgavKVh/VU/uGS5D0kU+O2Jqmxqpazix3MysMj8Etbmo6S8SLM1Ac09KGtEd\nQjc24OG0xs30Jdy+lMwCj5/8wphf+qXjrxUGBMXpmRwZhsGgLLCt9klQa7TYmuYLB6B22Jok1i0A\nMkM/9/agNsKQxA1ZiIdPjUTazuZYZYG79or2/+PE92Sa7KwNOevdeWp+7mwGhedzae3FZA8+655G\nYY/zzkPu3Hny9TwHvJKBpnPnSas7ZJqHhj9744LcaPHjxUqg1jAcnKBjanerMWGqJ2eG7iAR+x7V\nch6099otGkOCod+wSx0Hp+lA7fZ8G1sGhG2rxY5bhkELmI0gTmpmM2h7U3pZrs1ahpZFKW2k3TG1\nH41vY+UVItK8npTMgwDmyyiV8U2U9OlpShk7ht8iqHIePeqang/TOYP5Qvs9e1IihYvX6xqfD5pb\nhLmenBkO1nOfZjZlcxNuje53BkCt3u/9wGRPPEYD7SZTQk1/AyUENqBQh/LjfPARw7LVYn5hyay6\nDo41ZTaDaVljFwvOnvsRvesJQeJ5HIROv/UWpGqGkdda3npdc9vEq/Jj8uNgscDWPAcEStFKl8XS\nWX9e1bhFDo7efhYqxSwMqObTQ/lxKcFoXuxs6Ibvk7sRN/efruK5uVthlQWDMxpdqO+rUCliKfAS\nm9vJh1y+DNMyI8xy7SaSZ5rkQN2/Stu2PJzVuHlGo7lmnbQMw2C9KHD8/Kn7PcCizFjTVOTAgbGT\nxbBUjNIRw2G3DQ2SGKuvSaYASgn2k+OgNq4qDFqGmyd3k/+sfnjqpQC1hmHgtS0N8hDUzoqaMNOX\n4gZKMfFdsln3w0jqznAi2NJj3yIpif0AmcdHM7VNi9QEU54Q5ErhFEegtpA1g3mCHel1zQKpjoHa\nnXmNlyYYuocgIZh5Hu+8MeVf/AuOsXu5adKrT/fxWmsaTFMegtq2hb09KAwDWzOiwRGCAghaRe7e\nAiAXJVGil3t7UOtRxNQL8de2D2MQHq9rs10Gsyny3MlzO7XvaSn1vejfe2pW7UdXG1I/4uLa5qnd\n09A0mbsuF+w9rl598vXtbSCSrOuES2uUIwSVlJh1Nxu5m23jWH38+Vx/41/mg1p+N1M7aeb0khVA\nrZQdqF1KIK3z97HzgtrSv7/UcfDbjHfegZuTm9hmRKAp9zYMgwCwDI846wx6Wj+ml+b6TK1lkVkO\nQsyZTODjyV3srEb29Na2SErmYYBYMm5X9q5gmD6e5nPWRaopgrLkwQPY3KoZFxn92VwfhCoFwsEJ\nO1C7K+7Szyp95lcIZkFAPZt1oLb+V4i2wdB8bgIpWdgOatH9Vpq2YZol+AbaowKBAYayDpnaeXCd\nYdHov2cp2e8FeIyYz+Fb2zews4Thll7mtbdsALVLUPvqq9C4GW1ia71lfxn55VXFofz47mKPfpxw\n+eKPa91jsDR4XCzVLXHVuflrj3ItzbHqeHYIagslEC+40bje6xEHIfdGd5/6+rW9nMFsgndWb8b8\n8bKXsWFW6bBHx9ROypxekus/e0qR2DZm5nJ3ss3OrMLNU9A0ntKprbZFOuKZTG1RFXipvgrKl5KF\nZdEv5WGurOkYqKzS8iaAbp2StmQ/OZ6I8KgoGE5HrJ9CCsJn9YNbLwWoBfANaAzrENTeWTyklyx4\n6/JPal0vlJLx0twAlrm3RapvVKIUseeh8viQqV1gYNd6QdqelKTSxC6KQ1BbmhXDeKGVewvgmxa1\n6syxNjZgL67x0xS5whxb7Lts9WbkOVy/fvRa7vqsidOdbdiUktZyDkHtdNqN0Ratgan5pB8cwIVQ\nlMEt2ralkBV9zRnKg9qwbXbWBpwNbz11Q7ke77MxmaLO683Had2TZTGKepw1Hz4V1H7zyh6Vsljb\nOL17koZBWBT03Ixr1558/d49qHybrVOSbBmGgVPXiKWf3Li+i2f1cdNUO5MxUopCOVh+ynzeMmdB\nfwWA5wtB7Ps0B3au64+ws4rK1u+2Z45DWGd84Qsdaymkj7+Cq6hvGJiGzzxLuLc/BmURxPqsZSgl\no16ISMcIAden2zhpjdIwI4GD9dxHLBffK3tXqJVLTxfUSkkiJVFVcvs2eBeuY9sDoslkpdnpRliH\noHaqHnagdqX13KOdd0ztI/s72EVF5erKXLvUAjPpQO21/WuY0ieQ+g2oSEha6ZIkbWcIZt+hn5Xa\nwCJSikno47Rj5nP4l7d+Gy9ZQcItBInrYSw6UJs3Ca0jMGK95rsjBJVhYFc183nH8H9372M2FwWy\nr3sOMKlMlyRfgloSZDLi7dd/Sut6Byq1Op4zHMLuuKE1oP0UXIefVxuuy05/wGz3+lNf/3B/xPp0\nitz8dPar0DBA2ST+h1y61DIuC9bmi851U6M8KUn6faLd1/jug4/YXdT4eYKhkWesW2eUCVbE3Z0n\nzT4BKhqcFYwhPSFYKEVYGIyyDoRKWyCyVkuODxAIgbB4Yqb26nyPzfGY9df1VBaf1Q9HvTygVgga\nwz4Etd+b/R5rcYM50BtgD6RkEngUs+6Hdj8ZwWLCj77+01rXi6Rk4flY5YLtbdjaaokNgY0eQ+hL\nSSYk5jJPrm2hsioG8xRLc6Y2sCwaZVPWXVbtfloTpAnmCjNYC8+jjqf83M9xTIKcehFnbD15lG6d\nsxwqp8ej3e4z391d5voZEsvUNxEKDIPSAJpOQlOqikGcwgqbT2eANGTTuvNUUHsrmXBmPGHrC6fH\nim6YJvtByHq7+1RQ+zt3H9CfzzA2TndmZViXWFbzVKb2/n3IvIBX+6eThwzgtQ2GNMhzKJx7OGZE\n2DTa7FOkFIXpYnoJu/MprfLppSvM6ErJIvBpl/OgRbCLtaioHX1Qm9oW/bYz6rs5uQnSIZD6cVOB\nEAjhEmcpV3evo9gkmM1WAmSTKKCejun14E68g5vUqL6uO75k7vmoeSed/XDvCpm0CTXzmX0pSQ2B\nX9fcvAnG5vuE9jpBVXV5QTrXNE1KZeN5MeNZTmbN6GcrGE8tzbGYzdnchHnvA9yiptbM4QyW0U1m\n0jV23733Lo4ZdQyzZnUGjx7ztGBnOieWs24t1gW1UjKOQtxmynwO/+/Db+Ev2pWASmbbiEUHGH/5\nxi/TKhdyvefwILVAKcmlS9DScH16n8Fsof2eA6WYBh6KMVUF061vsFa0rJ/Rk20GB3PEs46pfTQr\nUWVOGOmZd33S2jBNHg7XqPduPPX1j2djtsaTTy1WMBQC4YJROWTmAyZlwfo80f8epCTu9xmOL/HB\nztXuPJYlyOj0zk2blgXWOh/v3X7q64UU2JW+m7AvJYlSBHl7yNQaSmLoJV1211QKw4bJ983Ufnd8\nj7OjCf7l0zszfVY/ePXSgNpgmb3Y68F+usuD8h5rKwCLUEomyzkQgP/9vf+b9VpoL+zhcn7oANTW\n0SOoK147+5bW9aRh4LQNNZLFAtIU1GCMU6QYuocgpYgdhyyZsbHRmW0F6QJrFVDrujSL+BioXZQp\nqRtybv30pLMAZ1yX0l3n/qhbPHd3YXOzA7X2Cjb5gRAkbQ3jS1wf3aJWVTcLp/m5wdIAqT9gzdh+\n6jzLlUczzu2PeOOnTyfnF5ZujEDQJk8FtR+Md1mfTbtOwSnW0GhBqaeC2qt3Z6RuxIXh6bHHPiCU\nYDQC78w9hPTpNfpSu8i2KZSDchLux3ex5Rb9RbzSHFPs+4jlPGisRtiLhsbVv15um6y1nendzclN\nKmHhawI86NYiIcCMWNUAACAASURBVDoJ5I3JdUxjHX+F2a1QSqZRQD2dEG5MyVpw0hJroM/8LhwX\nlXTg5IPRx0habE1FgFxG8CjD4tYtyKL3cVTEKt7mvpRMQo+ePebDvQ8x2/P0En2w4x+4FS9i+n2o\nNq7h5g2Np/8ZJtLESjtQ+83730QqfyVQGypFrTzmacrH2W/Tsy/Tn0y01+JIKaZhgFVNmM4r3p98\nRBSvJmdOLecQ1P7DK/+YRiiaWt9U0DNASIvXXoNv3P4GlvQJy1JbGeJLyThwWffGLJKG7OJvcKYU\nK52lYtejXcQMBvDN8h90s6z9z2td75NWt4f2sSZPlx/fT2Zc2B93h4BPoSIpafwWL3mbK/sfMq0r\n1qex9rPSk5Jpv88wPs+V3Y8YZTVhGqP6p2cOuel5FO6A25OnGFYAue3gNSsocpZuxV7eHILaRpmQ\nGvqg1jRp7ZZZMTmM5AT4aPKQ8/tjepdP7/P7rH7w6qUBtaFpUiuHKGq5f/Z/ZWi9Ti+ZrdQlm/td\nfMG1/Wt8497vcr6S+gu7UiSOg8pS6qbhL3/jv8GvS7bOvaF1PYCAlkrZLBbwL69+AzZvEK4g6fGk\nZOy75NMRg7WSB+bvEaUzwqHeon9wCGriOV/5Cvz6r0NZwtXpQ4Jkgf/qOe171akz/T6p12d72lGf\nu7uwvtlSCYXSDJaHZSe6rpHxJa48vIVhVVhVCSsczjZMk/0wYlA/3Xnw9t054WKBofTZMJ1ar0qU\nNLj3FO+Ne/mYrcn0UzskfOJ7UiaVcp8qP/7u/m28LME6xXvyhUBJyb17YK7foxEOq0xBRabJNPBx\nxYRH6R2UsdGtbSsAvHkYIGdzsiojFQXuotIGJ54Q5KaitzS9uzm+SS4UwSqMm2nSSoekSLk9v46p\n+njzufaceiAlsyCgnU2xz15j4F4mShLtmdpQKRa2gxmntG3LldEdorpZKXc7EgIsj5u3GibW+wjp\ndZJGzfKWUSp9a8QHo++gqleIkhVmdKUk8TyMecxO8hBccJKadoVYpEwIzKxTzrx7711qaePZ+mtx\naFnUpkucp9wV36DnXiQajVZiamdBgFXOGHu/Q899hSjJtUFyeDBHnKTUTc0/uf7LuFVDZuqvEJ4Q\nGMrktdfg73/n77PunSdY4RxwEMU38Mb80yu/BIbPoKi033OwjPxqFwv8fsK3w79NsDDgBTOOG6bJ\nqBfhx09PEJikI6J4rg3+v79C06TxWjaNz/Ph7ofMioIwSUDzeY6UYhpFrCdnuTb+iGleEyULrP7p\nNbI3w5DUD7kfP52pTVyf0ND//DwhWBgGTlYegtpaWrSZqX188k2T0jawsIiLowzsO3t7RNM5pnO6\nZ6bP6gerXh5Qa9k0ysX0E6Zv/h2G8jXsotGWbh2aG8xn/LVf/Wv87I/8BwzjhTaojaQksR2sLMH/\n2Z9nv0jZaPVBMkAkDAphk1/6J/wX//w/RFWvsK4pIYQjyfXf+62f579873UmwYesjRVvvP4T2tdL\nHfcw1/C11+Cb34SP5jusT8Z4r50uU7vV67HfGzCbbgMdqO2frbGqAkL9w2hgmsRti5Vc4sPtW5hO\njdKUlR/U0DRZWBa9cvpUUDuKM8IiW+n/oVNbLbTSeYKpbVvYY8bZ/fGpM7UX/IDcDrl/f+l2/Fhd\nz+537oxn9SI4dMpXCiktbt8G0btHYVisMgUVKcV+PyJkzG5xByGG9OdTbVDbV4qZH6DihLvTuwy8\n8/hpTruCy/lCCLyke+ZvTG5RGAJ3BXASWBa1tEiKBQ/S69gywG9brdgtWEr5Ah9mM8TGNVx5kcEi\n1gZ4kZTElo2V5Owle7TSo1fpz6tCx8wgAm7ezXjUvE8lbMJVJNzLCJ5Ijrk2fQ+jPks4X63Rmzou\nIl7w7e1v42RfwF4U2qBWGAYOLdSSpEz4aP8jckPirwAyQtMk9jzyZMaO+w0iZ5PeYrESsJj7PmYx\nJ976l1wI/gC9TJ/t7ilFbNmoNOO37/02G9FFoqZB9PVBrS8lSIsLl1N+4cov4Fr91STcS5frgTPm\n73z7byF3/3An/V8F1NoORrLgG/nP42VfJFjUGP0XOxs6NE0S2yLIpk99PaljvOwZAawaFZomlQuv\n+m/zwe4HxGWFXel5pkD3rMzCkI1inZuzq0zLDtTaw9PbXzd7PcZhj3H6pIS7bVvmYURo6695phBI\nw6AtjpjaStm8dVlf5RMoxdSL2KJ/LNZnL05w53oRmp/VD0+9NKDWN00y2+X/uf5/0u6+Ba2DWesf\nCMLlZvbB7d/lt+/+Nj9x8Q/Tm05XkuCkloXn3Gf64/89f+Mr/zNRUawEakMpSZWF8Sf+K/6Hd/4p\nrWUSrnAg6EnJpBexs3ODv/fVX8C8/3XW5wvsLb2Oqi8EpRdy/96HAIcS5I/nY86MJgRvnB7QADhj\n29zfWMecdcYRu7vQ26qxy5xW85ACS1BrGLj5Ja7t3EK4LW692uIpDIMzZYltmOztH59ZaVtI24Zh\n+2IjEZ5WF5RJ4fS49+A4aH/0CJx+zMZsulI+r0690l9jHqxx4WLNje/be7fbR5yZTODc6akCQtNC\nGjbXb5Y0wT0yQ3UsnGZFUjLuRfj1mFF9F4weg7m+gVBPKeaejzlPuD29zXp0AT/VN8GzDp0/BUVd\n8DCd4LWttms6dI2Bme/R5mMeldcxlYO/AmvZrecBxmxO07+G4hyDhb4jdSgliTKxk4Ire1d4de1t\norJcjak1TWrLZ7yYcz+7Rm7IlUBtT0omUUgkJtxIvkNbbRDNp/pMrRAktoNMUr714FuEvI2zyDAi\n/bXTB1rD5FsPvsWPbn6BhWEQrcLUSsle6FOmD4jDb2PbwWoqCSmZ+wEym1Nf+iWG5ue750YT4PWV\nIlYWKi34xSu/yL/7+r9Pr6n4+n+mf5eBadIqh93BP+GL575I3horqST8ZUPfG3yH90ffxs4/30n/\nVzj7JLZDNtnnFx7+T0T3/xJBnL1wUCsMg420whZ9ZvmTRke5UeKVnx7IiSyL2LX5wtqbfLj3IYuq\nxtI0AoXu2Zv6PptVxKP0LvOmoD9f4AxOT3W0ads8XF/HjD964rVxVWHnOdaKoz09wyCtu5naqmlo\nhOLVV/TJGV9KJl6fzSo6ZhY1q1qc+Uq3+ln9ENRLA2oDKRmFLn/3N/9HrH/1l0maEnsFUHsgmfm1\n7/5j/sbP/A2SxqC3wmxOqBSZMvGsHf7Aw79F5G0R5flKoHZoOWCFDP7xr7Ge/0FqpyXUHUSg23A5\n8yr/y0/9db7y+S+SUbOejnC29O7Rl5LSC9m7f5WmbY5A7WTGhb191IVTZmoti4frawTJLdq2A7XB\nRo1TZCvNvwaWRSwEQXGR29NbtJ7AXZGpBTgLOGKD7dHk2L9fvw51aHPu01FNnajOuS61u9450j5W\nV69COCzZyhanfk8XBgNGvSFvvLn7hAR54ky5+GjndJlay0IYLh/fjcnseywMQbTCITNSimkvxCsn\nTNo7tIZPb7YCqJWSmethxSm3J7fpB2cZ5PrGU9A5fyrp8vHoYzbDVwnqeqXrBVIyjnysYsR+cx3D\ntFaaLz1Yz8U8Jg+u0tSbrC30AV6oFAshsdMO1J4fvNGt56swtZZFbbmw+T3O+ueIW7SNp6Bbz8dR\nRGBMuVN8h6bqE04nK8mPU8tGLVK+/fDbDMw3cJNUO+sXOj+CFot3773LT5z/MkHTIFb4DEOlmAQ+\n0/bXUaMfpTCalVUSseuRjndh6z1kfZF+og9qPSGohEDkNb945Rf5Q6/+LEFRaDO/AJFlU5ke72b/\nB3/unT9H3DQEKzS3D1Rq7bn/i58J/iIqMAjH45WY2tSyuXbn9/j3Xv2PyOIzhPECOXzxLr6bdYst\nznF/9qQJROpKQs0ov6dVKCWjYMC/deYVPtj9gEUDVqvvDNxTipnvM1Q5G9YrzOSIQRzjrZ2eP8Sm\nZfFobZ0wufnEaw+LgvXJCLVizm9fCGYtSEOym8WoMkNF+s+GLwRjv4e1F/Dz/9uIv/AX4I/8EZi3\nFn52ug33z+oHr14aUOtLySRwcfKa4d4fJxYVXrUCU7ucB71sbfLn/40/z7Sq6I1G2iBUGgZ222Lu\nv8NPeX+WeV0TJclqoNbzqU2fXv45th+1NAq8FQ6SfaWY9HowHmPbYAYFTlqiPL0NMliaYmyUJt/b\n+R5f+hJ88AHc3ZkynM5OndE7Y1ns9vqcFXeJY9jZAX+txskzjL7+fE8gJXGvx1Z5hgfpTWpH4Rqr\nZ/CdM02ks8n2/PhM0K/9GhRRyFn3dCORAM71eqTeGqmxR/KYcuujj0D5kq32xWYPPq3O2jZ3tjZ4\n+9y1Y2ZRTQOGF3NuPF4pM/ik5ds2SJvr90Zk1hy3bZEDvXgNWM71hSFeMWUu7tAaNkFeaM9sH0gg\n7STn9vQ2gbdJv0gQof7aMZASzIgPdj/g/OB1/LpeCeD5S6M+s3pIauxSmgaDVt9l88CsRsQLFs41\nymrA+kIf4EnDwF5KZz/a/4it3mWiLFsZ1M7dAPv8u/zYmR8jB7wVm5TTMES1t2nbltaQK7kpd27F\nNmaS8a0H32Ldv0SQJojeCioXpWikw7v33+XzZ/8g/Uo/RxcODB59UvUrNLf+bRZUK6skFq7HjXvv\nYe/+IaYZDJOJ9j0ahoHfNkyrkqIuONd/nTDXn9EFGFgWkzDg/d3f5E9+7k+yAPwV9lZfCBaeh2Xs\n8E7+X6OinMFMf78OlSJRNlEl+O++/NeZtxW9OEYNX3ze6hmpaM0N7s2eNIFIfJf+p+hJESpFMuzz\nx78YkNc5ixZs9EFtJCVT16UnY7bMN0nMCeuzBH/t9Bq0ZyyLncGAteLBE69tFwVnR/vY5/Uymw+q\nb5pM2pahO+TuYherSFGasZTQrVPZMMKb+YzSMV/8Ivzl/zal9kK++sXTTdz4rH7w6qUBtQfzoH/x\nrf+UfiRJzRa/Xm2upPJ7/Oev/SmUUEyriv7e3kqbT2QI7OQiZ8/CrKo6ULsCmIpMk1mvx4Ybc2+/\nxiqalTLM+koxCcMuHR1wggIy/c+wpxRTpXhDbPIrN34F24Yvfxke7GR48enPg66bJjPX55y9w+5u\nx9Raw4owiZG91RbRuN/nXLPGbnWL0rZw9T+2wzrredTOkJ3kOKj9lV9ticMer6zQzdS+p+GQSTjk\nzPntY3O1V69C7bqcXUE6qFvnbJsHa2tcim4eY2p3dsCPSrY+RYnZJynfNJl7LtuT7+KrV+nVNawA\nakOlmAUBbj4nte6CkPhiNQZvblq4i5xbk1t49pC1fLbSIXNgmtRmwAe7H3AmukxQlisztdMwwLY+\nwC0ukpotgxXkx4GUnVHfImEir5JVARvz0UogNMSgxebK3hWGwStEabqa/FhKdnoB6+e/yY9svIO/\nooS7rxSzIKSsP2Cz+XFaqyBY4TPsKUVsWtSzGbN8RtQbMIznyP4KIHRpCPbuvXd5ffPH6a/IWoZS\nMg18avt3Me78YeZto50dfHC9he0y39+mv/dzTIqa4VyftQQIaSmFw9c/93UWTUOQpquBWqXY6Uf8\n6Yt/jMAKiFkN1AZLIH9h9FOku2eRQc6grrUjyXwhyJTJz67/JG+dP0MqKvrzGWrtxe9f5z2Xwhlw\nf36cqW3blnkUsr5CI+/7K5KSdL1HwILPb3yexJCYQr8R11OKmW0TGjGb4i0Kd8owTnFOMZ7ujGUx\n9lw2ioTi+0aq7iU5F/ZGrP/IiqDWspgYBkN3yM3ZQ5wiw1hhDegpRX024mfedvnjf3rMX/pL8Pq/\neYPUX+P85umZbH1WP5j10oDaUErKtbP8x6/8UaJeS+aAa6zmaFv4PV5rO9A5LQp6RQEryMEiKbH8\njDNnYFbXRPP5SkxtTymm6+ucscfcH1e4eb0SSO4rxcT3D0Gt9EvIVwO1c8PgXBvyyzd/GejmamcN\nBDP9WRPdkoZBr8jo+ekhqBW9iiieaQfVw2OgVlQYRoSqSwx/dRb6XK/Hwhswyh8e/lvbwq9+s0I2\nNRuvXF75/3Hie3Ic7pzZ4I3htSdAbeb1uNQ7/U7oWcvi0XCNPu8dY2rv3wcZWJw/5VXMk5L9wCNL\nv0PkXOpmLVdkamM/wM5mFNYDamXgKX15YaQUsZBYacXt6W1Mq8/5ZsJX/hP9DX9g2xRWwPs732M9\nvEiw4mhFKCWzKMDzrmCnr7NQLf0VHV0T26GajDCMlrSRrMej1STXUtAIhyt7Vwi9LaLFYjWmVin2\nohC/920ur3++k3CvCmo9n6q4Ti97B8OpVnLF7SlFIhXFfMpPnP0JRK9iYz5G9VdoDFg2tXIpqwLP\n3aSfZSuB2kBKZr6PX+X0Z19m2rZEq+zZSrGwHIICNuOvMqsqBrE+ww+dy3Vt+nztra8xr+tPBdTu\n9UL+7OU/Qdo0WG2LWqUxoBRNb4PPjf4YOzuAV6zUUFJCYAFbch2lwBzUDGZTrI0XD2pfHUYkUcS/\nun4c1E6rbg9du/DpsZ6dq3wIccxb65+nMiTKWkEloBTTJagdtG/SujFmkWhHNuqUNAy2qoqgusD1\nvePRSP/gV29yZjTinZ++uNL/o2/bTGybDWvAzfkj3GK1NWBomoyjiPXaPjSKen//Bo1QDE/RW+Oz\n+sGslwbUDpUi2TiDN0sJN2tU0SLdFTZbKYlNkwPr2WmerzSbA91mIb38iKldwXwBukVvNhiwZY54\nOKvx83Kl6/WVYuK6h6DWcCvaQr8xIA2DyDAwRcBv3fktyrrk534OqsDtLP3/f6iNKkcF4hDUtkHF\nYDZD9fSzywIpmQ8GbMgRTvM5nGxBvYJ5ykGd7fXY6/VJy6MN+coVkGcTNkd7+D/y9sr/jxPfk2Wx\nvb7OJfvGMVD74fWEOFjj0ubpbxrrpsnC9Ugnv3MM1N6+W1F7IRftT68b/0nKF4Kp5+HY79P3LtLL\ncxjqP18Hc3317CGiGFDZLYGl3zSRhoFLi6gVtye3UWZAb7SHfVb/HvuWxSQM+Xj7fcLgPGtxDGv6\nIHmoFONej9C9gbF4C7tpMVdo2IVSkpgW2WiXs9abFLIkTPKVYrcipWiUw73ZPSx7QBTruykfXG8U\nBtjiPuf7bxCu6KbcXxqCOXmGNf5xhF8TrHAglkvprCF9fuLs/9femQdHetZ3/vP0e/bx9tt3q9W6\nNfdle8aMDcY2xhDIAiEQFwVskZDskqQItcmGBJJsbZGkdquSbDbZTWWPYsNmCZWEpBICLAECBpvg\nBGMbH9jYnsNzSiOp1Zf6Ut/v/tGa8cDaY4+eVxrG9Xyqpkrq6f7paamf4/v8rsN4kT7ZehkjIRd+\nXA+FuD17dBQNtb4u7althMNECtPEIzH6QFDCaxnd+NykvSBZDtIedgl3Bpuuwg2jcEvPdLhj+g4a\ngwFOY/O9TGEUJdHO5rnDPUSp1yPZ60mJ5ISu04zGyfUFKyvQD/WlLpRglHPfaIzaq1iJPplmGSuz\nDZ7aVJxaLMpnvniOy7MXzq13SZdLuPNyXsbLieo69UgEGg3mUgcwuk0Goc1/9lxNo6brhGkSbO5G\nC3bQB9sf4Zb3PGxtnKfOP9/Wp9mEx06fINpubtqDf5GYrlNNJskJh3ONVUKStQkSuk45EiHZNy8V\ninqsssB4pYSYkMv/VVz/XDeiNmkYlGIxKJWw033sep+hs/lFM6JpNDQNyqMy49VeD1dyYY9aFoFg\nbyRqBwOi1aqcp3ajOXdar1BoDEbeEVlPrWleErXdoIe+LleNKKFp1IcwH5/nocWH2LsX2q5LtHtt\nEvZzAY9+JHRJ1PaCPZJra5gSZfLjG7nIKVFCdOYJrq9JHVIujdW2WRjLEu0+d2lDvu8+2HVXkcnV\nIoEp/zbklz0my2IlniQvzl0Stf0+nGldwOz1iOTz2z6mgBBkel2KlSUqFdg4O/H0+Qv0gmkmJQ54\nm2GU3x8mFj5BPDI+CkuV9dTaQfprK4jaFH3Lw5HMEXZFgEEgyIX6BRCWVMsOGM2B5YTLyuJxgnZ2\n1EZJQtQmDYNK1CWqr9Kt7cDtDSC1+bC7iKaxro2qFU+EdjK0O+jdzXvwAFzTwtNCzMZnaQ690SWl\nZEufSiRCtCNIR6dxul15UWuHcLrQXzzEMDIkIbmHxfAYmA5Hckfo2T3GakUpURs3DFZiDq9N3Ei1\n3yfW3Hy7HBhdHDdCYSJLO3HG+kSHQ6lQxrCm0dV09gZniDoBuoEudl/uEJ8OORh2AkMzaAwGRBoN\nqbkX03XqqQyBUnkkaiVzdJOGQcmyiA1KrKxA1x4Sl7j8gY3z1Po6AHqsT6ZR2nQByqshFwxyITeG\n23mOT37y+cdPlDtMFFdxZnf49rOcjSgB6nUm4ruxOk0Gkc2v09GN9K3ooEJveRfC1NG87W9JM2EY\niGCSZ5efF7V/9EdgZ6pkDHnnREzXqSYS5LwI51sVYk25S56EYVAOh4n19Uue2qdrRaYLqzA5KT1e\nxfXN9SVqIxEolbCS/VE/Kpm8F02jLgTDi57awWBUHVgCxzRpOyY7J9sjT61E4SnYyLmIRkkFypRa\nA5z1tryoNQyoVhl6HutB0NtyojZuGJQHA+6evZt7T93LEI+WE8MNbG8v04tM2UGaTpSTp7toGjRE\nn+RaDTOx+QNzyjAoui5JSgza04TXG3iuvJAaN00upNNMaGcvCbX77oP0/iLThRJcAwGZNgwatk1i\nUGBho/bGmTPgHFhmrHJtxgQwITQCgwiTB09z8uTosWeXz9JwEoxLCMrNEN443Lih07hOdtQnU9ZT\na9lo9TqD2jRDDYIS6wZATNfwAkEy4Qy1dmdUTEfixj2u6xRjDsH1PpoVJ1kuS4vaaiRKtAPNygzx\nTkfKnqNpNDWdSFcw7+5ChPtoPblCa45p0jPD7E3sHq3nxaJUj2ZX11mLRMh1x+gJfVRASDLypmEF\niXYEjbN76UY8krKiVgvQN6McGT9C2+iRrZewE5s/uKcMg5VEjFvcfSNRK+m1dDSNnhMnfvYQ4fRg\n1DtYwp4QAgfYFZ4nGoWe3scayv0OXdumZlmwvk613ye6tiZ9oVSJRqFYpNTvk5QMZ04aBiXDwB2U\nWF7xaFsecYkQbgDXMKh2R4JMd3okm2vo0a0v3jdumlzIZLghfo6PfvRS4B1PFteYLhSJz+337Wc5\nmkY9HIZymbHYPMF2i6Gz+UupYCBAXwjsfonq+XHQwugS1ZQ3y0QoRNeJcap4Dhj5ef7zH3h0ogZ7\nwvIXEzFdpxqPk/VCLK43SNbr8p7aYBC3F7gkak8360xfWN7W1n6KH06uK1FbDgahWESP93AabakD\ngR4I4ApBuTPK/VzzPFyJMvmwUUkxkybcrYw8teWyXOGpjUNQQlQot/ujUBBZkRwI4FUqrPX72P0h\nPV1OnCUsizJw99zdfO3011jqtIk069zxzgNSdjfLofwsTSfNp2rvJ5UeUun1yFZqWPHNH0ZThkHR\ncYgNS3RbeZxmA+FDEacp22YxkWIisESpNKrme//9EEwUSNbKIPl53AwBIUi3Wwh9cMlTe/w4uLsq\nTKyW4BqF90xZFlZkivDB+y4VizrZWiDY6RDcxnY+MBK1jXCEmLeGG02NcuclPbVNw8LtgNecxWoO\n0NObF8kAMcNkaISZjk2z2ukg2/kwruuUYi7pnkE3YJNcXZUWtWuhMNEODDp5Eu11KU+tHgjgeEPQ\nHXYmdzIMD9E9uUP1KFw4yMHw3Gg9LxSkRG1U12lEHQ6E89T7fZxmEzKb/8u4uk5TN8kOXAprOmZX\nLoQbIGGa9O0oOxI7aNIlVOsRDG/+mJAyDBpjE7zKnh+J2lpNrr2NptF3EqSrEYKp/iifXTJSIxoI\nUGs0cBwY2ANCQ7mcRlfXWctkoFhktdslUyjIi1rHgWKRcq9HstmUO1doGm0hsAdlCvUBxsDDkIw8\nSts2q6YJgwGBcBuj6UmHrb4cpmybhUQSt7jMu98NH/nI6PEHz36bdKVMWLLH6uVEN0LpKRSIhDKE\n2+t4EuJMbKRvIepcWBR4WhhdXIPe9PE4jWiUs2sjT+3v/A7c/a5jtJwM+1Lye2tM16lGo6QHNoVe\nh+xaTeoSOGEYVCwLpz3qfQuw1uiTb9SlauIoXhlcN6I2oeuULAtKJbRYH6feIhCT28zSus5qr0d/\nOKQFRCTaK8BGFdNUCsplau32aMOVmGSuprEWCuF6FZpGj8zampSn1ggEsIBmo0Gx1yO+3qVtSR6C\nLIuy43B7+mYeXXqUR0rnmCgWGH/NLim7m2UmkWQxO47XOEn39l+n1OuRq9Qx45Ke2mAQt1+i2x7D\nbTYISBSeukhc1xEBQdZoUSzC9743+vNWe3XczrXrIj4x7NKy7O8TtebEGjPLK5Ddvh56lzPnuqxF\nMnRyX7uUV7sgVsmXSjA/v61jSRkGZTdGrA2OE8ddW5MTtbpOQ9OJtcFkCrvVQ8/IiVrXshiYEabd\naVb7fdKSUShxw6AcddmpZyn3+iQKBSkRmtB11oIjUQsZEq2mlD2ARAD6dozd2R0MLUCXu3hydJ1V\nN8J+e2q0nne7Um3KXE2j5YQ56GQo9HqkKxUpUWsEAph45Iwp1rweTr0rPT/HIi6YLgEP1vodvKYl\nU2trVJE+O45RLI9EbaUidTE7WottMhSwkv1RPrukIIsaBrVWC8eBYWhIMCB3mejqOmvpNKyustrr\nkV5Zkcup1XUqwSCUSpR6PRKS4cxCCOJC0DfWGQT7OJ2B9MVA2jRZHRuDSgXP7qG3tudoOdpDA0Tq\nGv/+N7v8wz/AN78Jp9fOEK75KxAdTaNm27C6SmMwxO108SQ/e66m0afJwgWPnhUmxPaHH+fjcZaT\nSeqlkywswCc+AfvfcT/rwSjjPuz3l0Rtz6DSH5IvVaXWqbiuUzYMnFqbynqFwXBAp28yLdESTvHK\n4boRtUnDoKTreKUSOH3cRpNAXO7QkrEsCkJQGwxwhkMCkiF/SV2nmE6PRG23O7qFkyCq69Rsm9ig\nDPEuuUpJbgnpvwAAHGlJREFU6kAA4AYCVDsdir0e6U4be0xS1Oo6lUyGcKvHkfEj/N3pB5hbWoW5\nOSm7m2XKtjmTn2DyL/8D9fzneLx4kuxaTap1RsowWDVNot0SkCReb6DF5EQHjA4XM70eIStIqTQK\nPb7rLljoQbrXkLa/WaYNnXrQ+T5Rq0ebTJWLUoV3ZJhPpVhwE6xoX+PY8dHmVQ7X2XPu/LaL2qxh\nUHFjuB2wQ1GilYrUzbMVCICAYN/AMieI1tbRJD21rmlSDUeYD+YpeB4ZSa//6GDiMK+nKXW7JCXb\nnyUNg5ppE2uGMNwA8XpNWtSmdZ12MM58fg5jfUg3LJcCkdR1VhJx9pi5kaiV9AJEdZ1eNMFrQrtZ\n6XbJFgrSIjQe0BgzxsHtEW+sS9tLWBaVVAoqFcqDAfvmw1KaMWUYrMbjsLJCtd/HXV6GsbFN28ua\nJgVDJ0MBM7bRC15i7gFETZNat0soMmQQAkeinRY8n0NIsUih0yFTq0lF3cQNg6plPR9+XK1Ki9Ck\nrtM2ehDpE5UsQAmQMU0KuRyUywzsPtr69lTwFUIwPxjg2DM0xAX+8A/hA7+0Qt2wSK35W2shaRgU\nLQsKBWr9PpnugFhWrjJw1DDo0qbQ7RFsd/CC298yb9KyODU+QXT5FL/1W/CzPwvfqj/GeLWCNiX3\n/mB0yVN1XdK1AS0MJlbKUuuUo2m0AwHMSpNKu8JifRHdzjFtbv/vTvHDx3UjakOahgBatRpeuE+i\nLtd3ESAdDLIaDLK2vj5qCi+5sOcsi6UNT22x25Uu2uHqOmuGgTOojERtqSDlqYWNDbfXo9jrMd5v\n8RP/Ss5eXNcpp1JQrXL37N08eOpJxspFXwopbYZp2+Z8OsV0q8Sbil/ifL2M1WlIhUIlDYOyphHq\nlkBzSdYbGHF/+qHtsG1EME6x6F0StRfMMFmt74v9zXAoN81aJMWS8wUGAzh2DHoW5Afr12xMc+Ew\np6enmW0bfPfCM3ge6JEu84UV6Xl7tWRNk6oTJbYOQy2IWypJeWoBYoBnuljhDOmyXHgWjOZ5IR5h\nUsuC5xGW/B3FdZ21iMO0iFFaXycJUnMqpGkgBOF+AivVJ16pSIvabDBEP5Qg7IYJNbp0o3L2cpZF\nc3onB73MaD2XvBhwdZ31sMtYbTgStYuLUp5aGP2dW90GuD2S9Ya8SNZ1yrkcg6Ul6p5HKiW3P6QM\ng2IkMhK13S6xUkkqbD2oadhCEI6uobn9UZSEZJ5/wjQpJpOY4RLBtQGeZMiqq2msue7IU9tukxnI\neQzjuk7FMEaittcjWZHrowuQNE2axgCc/qjNkqyn1jBYzWSgVKJjemid7UudmTNNAsE8i7VF7rkH\nAof+ilZ4mmRf/uL5csZMk4KmMSwUKPZ6zDUHHLjxR6RsuqZJU/MIxNZJNPpMzd7o02hfPjtDIU7l\nc2RLy/zdZ4d85CMe31o9zfxKAXwQtTFdpxqJkKh2CGgu2XJV6hx7MdKg1epQWa/wXPk5euEMkxKO\nC8Urh+tG1AIkAwFK7TaDYJ90o4qe8uF2MZ9nrVwe9aiVFbWmyVI8DuUyi4MB+eFQyl5C1ykZBk63\nDPEe44UL0mOMmSbVwYBir0eqVpMWyQnDoJxIQKXC3bN3s9bRGauvStmUIW0YtE2DSfs0c/FZUs4M\nEU/uUGEEAoSBvmiBrZGuNdETPonaeIKuM865QpV//EfYdXSFihlkPLW9xY8uZ08uz2JukvjrP8Dx\nc1WOH4e6HWEitP05vheZCwZ5LpfjbfYhTgy+Tq0GwajBbH/7w7XGTJO1UJh4K0i11ydeletrCZDT\nNVqRFGY0Tq5Slha1rqZRdB2iIkmm15O2F9d16mGHGS9Gudsl6UMvxSQQHezAiPeJF4tSYgdgPBKl\nFk/RqK8RabbpxeQ8tTnTpJDNIRYXR+u5pKhN6Dol04QLF1jpdMguLclfXtg2je46uD0ytYq8p9Yw\nKKfTrK2sEB0M0CTtXUzdYHmZaqtFzDSlcy2zmkYg3sVzesRLJeniMHnLYnFmBqEVcEstBmNydQNc\nXWdtIwe20OuRllwbLvaDH14UtUtLUrndAMlgkDVbJxDpEF9vyXtqDYNCIoFXLrNueAQkWgVeLXOO\nQz2cYbG+iBCg3/xXtCJR3KC/tRbMQICoEBTX11nsdskvLvpyobKazRGNF8nW2kwduM2n0b58MobB\nQNPJ1dP8/K8ss9x/FtPOM3vuvC81NGK6TtW2iZab6JpLaNiWXgMSuk59vUu1XeXZ0kmq4STzqp2P\ngutN1BoGpX6fnt1jqn4BY1aufHfaMFjNZilVKiRaLelDVc40ueA4rFcqND2PlGSoZsowaAkBNCDe\nYXxlWdoDGrMsqrpOsdMhJZmjCxuV6FwXqlWO5o9CZJbxXk3KpgxCCHb2etjTZdJpqHuCnePy4akp\nTaOpdSHSZ6bcYWr3LT6MFuYzGVaSOb7xwFnc+Wf58XvvYbJc4jU/8tO+2N/UmIJBTk/PcMtzr+bD\nX/llVkseJSfFTO7a5NPCqFDUcjTK6wc5uuNf54knPIjG2S2R47hZIpoGAUFMi3O+2WSq1ZLepPO2\nRd1JobsWE+VVabGTMAyq2TFSgQzpdlt6bYvrOj03zWuZpjQYkPShiFnatLB10GM94pI5uiN7JquT\nk5SWlog3mvQl8uhhYz2PxVhfXKQJpCTFScowaAUCNAsFVlotxgZy/VBh45LSjWK7dcaqRanQXthY\nzxMJyqurJLpdaXspw6BoGM97aiVbVQFkbZtebEAr0mHqzBlpYTFhWSyOj9PTKqTLa3jjkkLFMCiG\nw/SKReqeR0Jy7mlCEBGCtWaTUqdD8sIFaSGfNAwK6TFCzhrJtYp0K5S0abIai1Erl7EGHm19+y5l\n5zMZVmJpFstnOVk+yaLXZqpcYfcb/U+ByhkGF/p9FtttJk6dkv47TFoWZyenCccrjK+Vr0lLGiEE\ns60BIWOON95zjvvO3MeO4H5mOh2QrDMDz7eSDJVqCMMl5EMxrIRlUel0COo231w9xUS5hLVvn7Rd\nxfXPdSVqE5ZFud+nE+iQaxSxpuU23IxhUEilOF2rMbO8LJ2blzNNlkIhFlstxns9hGTohhCCyUCA\nctBDS6yT7A2lD0FJw2A1l6PYaJAqyefoJgyDcjQKlQp6QKcRnyFnyHmoZbnRsujM9UmkhzSBqA/V\ncVOmSU0bYLpNUq02Wtyf0Ka5UIhnpidZWvw4y2++k3fc9CH2nlsgcOgGX+xvaky2zXPpNG9cvotv\nLd9H8k1fxmk1yOy7dmPSAwEm+n3CzSDe9P188b4yTXecfantF9pCCNLekGx8gnOdDlM9+TYMs9E4\n1fQ4WqzFVGlZWtTO2Dalub20y00yjYYv4cwV0yRw7hwlzyPhw2VCIhhkaK6jO+1RqxeZikRsrOfj\n45wujnoWekl5T+1SKMRiucx4t4uQ9I4JIZjUdc53Oix3OmT98HYbBqvT04TdKrmyfCG3hGFQiUYp\nVyrEWy2QXDsvVtrtFIusDgYkfUhLyQaDrMWD1PU6U4uL0hc2ectiIZulFaiRLxURk3Ienxnb5kwo\nRLFeJ9HvE/BBqMQ0jUqnQ7nVImFZIPnZSeg6pVSGkFMjVVmFmRkpexnDoBCJcLrVYqrRZDCzfXUO\n5hyH4zPT9J9+ir948i949c572LO0wN63+j+GXDDIErDQaJBvtaQKx8HosvZcPo+VqDNeKlyzPqs3\nxIOY6TzL62e5/8z9YIyzz6dKwgldp6xpmKtlBqZL0Aez8Y0CpRMixiPVVQ6cOg1K1Cq4zkRt0rIo\nRSLUulWGVVOq1QBs3C7G45xeX2fu1Cl5UWtZrJgmC90u+Xoddu6UsgcwZZoUHB3iPZKS4XQAO4JB\nTszOUmy1SJ09C7OzUvbiuk4lEoHlZZa7XewhHL1FLs9EloOZNGvzOsF0H7ffJyC5YQOkbJs1wyMw\n0WCy718RjIORCM/MzDE7/md8ZO6vmLT2sKNYlPZayRAzDOLDIfFEganH/4Tibf+F+cVF3Bv98U5v\nlkOWxfFml7A3xt+e+jx9I0jOh5yfzZAPRTAGERYHA/w4huRtmws7dqKFK8wWF6RF6JxtczqVYrVU\nIl2ryXtqN7yMxUIBw/OwJSM8YLSeLyeyGE6VzHAo7e1OmyarySSn63V2Li/gpSRFrWWxYhgsNJvk\nm03pkE+AqVCIc8DKYEDWBy/IjmCQk3Nz2LEG46Ul6RzduK5TDoep1Gok6nVpT60QgmQgwLlejzKQ\n90PUmiZnUpNUh2tMDwbyURKWxWIiQV1bZ7p0AW1aTtROWRZLhsGFdpvM+roveYkZy2LZ80aeWh9q\nCCQNg2IqhZFokls8J30OSBsGq8EgpzoddlWL/Ni/3T5RezAS4amZWeynjvHnT/458+7N7FhY2JKe\n6uPBIEvpNIutFnkfiiZO2Tbnsln0eIuJlYVr1jJvbzpJyc1wpnKa+8/cz2k7zhHJPeMiQU0jFQhw\neuAx0G3MsHxoesIwqOTz7OhFKNU99vqQyqF4ZSClCoUQ9wghnhJCDIQQh6/wvDcLIZ4VQhwXQnx0\nsz8vaRiUcjlOeh2s07p0ZETaMCi4Lqe6XWaPH5e+rbQCARzP4wldJ7+6Crvk29pMOw5n3BCeOSQ6\nKTc+gN2hEMd37KC4skJKsk8ijA4Ep10Xvvtdnm422X/mLGM/8k7pccpwaHycp+bnsL1jo8sFP0St\nZbGcTNLLDZiy/SuClTVNnL7H3JM/zy+85XU8WCjwKh88f7LcEghQzLV49ot3MxZ5E0efPYY5JXfw\nkR7T2BjfDgbZY7yek5Pf4MaTJxBHj16TsWQdh8dTKRL9PpbkHIKNg/XkJP1wi8mifJrBXDDIc+Ew\nhXp91DpGcsPXhGCnZfFPoRDJXk9aJMNoPV/ITrOW6rC/1ZK2lzYMCrEYp7pd9lw4gzkudzFkBQI4\nQvCEYZBfW5NeKwGmQyGOz83R9jxiPoiT3aEQx/N5QrE6bkuuhRyMPodng0HOdruMr65Ki1oYRbl8\nK5lkR7OJ5oO9MdOksWOcFa3HlA/epAnLYtFxqAR67CqfxZyTExZGIMC4EDwSDo8ulHzwvu1zHJ7O\n50eh/z4c3pMbtTAC03X2La9I1wRImyYF0+SU5zF37ty2VqTPmibR4YDS+SKD4YCFmserPE86qu2F\nyFkWS5OTLA4GTPgQSj9t2ywk42iJDuMri9KREZtlbzbLM9MzPPHgZwmGJ+h5MOnD+fUiu8NhHkwm\nCbfrDNLyoel50+T07CwzbRtTn2Cvauej2EB21j8JvAP4xos9QQgRAP4YeBOwH3iPEGLPZn7YrlCI\n+2+6ifZwwO7plHRv74xhsOo4nGo2mev1pMruXyRnmjwSCDBx9qw/ntpIhG/fdJhUtY62f6+0vV3B\nIMfzeZaqVdI+eANnbZuOrrN4+jQPnz3LDefPX7MQmoscCod5Zs8cxfazHDlzxhdRmzZNHjx8hES5\ngT0xLW3vco5oEVJ7m6RSHt8E7pAMw9ws999//6Wvbxkb40RWZ9Dtk4rt50i9vCWHhKvhlmyWB/fv\n563GfmITGq956nuwY8c1GUvWNHn45puZWlqCW2+VtjdhWSyk06wHO/R6OWnv08X+jceFIHPqlC8t\ntg5Eo3z6ttvYWSz60q94VyjEAzccZKB7vng9MobBaijEKU1j/4WT3P0ueeGdM00eSSSYWF31x1Nr\n2zx86BCZ9XXEZSL58rl3NewKBjmeSjFMdIn6EEEya9s0NI0vZjIcefppX0Rt2rb5xo4d7CoWfTm0\nZ02ThXyOSiDAmGTVXhgdkBeCQUp0yZSqBDPyl5ZzhsFDySSZUsmX/fBAOMxXb72V4WBA0oe/ya5g\nkO9N5Fif9dg3lBcEUU2jFwjw9GDA3PHj295m7bAeoBvawXsOvpdveh53XMUF1NXMvZxp8uzMDGI4\nJOrHemBZLEQjBOLrpAZIh5Vvlte6Lt/afwDrnx9h9/RbOPzcc4iDB32zv8tx+MahQyTqNQZp+XPn\nq6JRHpqfZ3LdpJ3Yw2FXPnJI8cpA6pTqed4xz/NOAFc6gR0FTnied9bzvB7waeDtm/l5r4vF+MwN\nN7D/5EmOvkveazRhWSyGwxxzHGZ9EhK5SISH5+bI+xDODKNF78F9e5msLGMe2tRdwPexMxjkeDjM\nsXicwz6EEAohOOq6PGQYfG5lhbd2OtIHclmypkk8EOBPhODmJ5/0RdTe4br89atv5Ybzxwns8LcA\nxev3TXN+d4Rjq6uEGw0mjxzx1f7L5ftE7eQkDx3Yz22BBzgRF7z1JnnhJsvNjsMTMzP8WL2DFTnE\ndK18zT5rWdPkWzt3Mv3cc/Ca10jby5sm5x2HshOkZL1o0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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# ------------- code we used to generate the bitstream and the signals ------------------\n", "#bits_ref=bitarray.bitarray((rand(4096)>0.5).tolist())\n", "#sig_ref8192 = afsk1200(bits_ref,fs = 8192)\n", "#sig_ref11025 = afsk1200(bits_ref,fs = 11025)\n", "#sig_ref44100 = afsk1200(bits_ref,fs = 44100)\n", "#sig_ref48000 = afsk1200(bits_ref,fs = 48000)\n", "#np.savez_compressed('debug_ref.npz', bits=bits_ref, sig_ref8192=sig_ref8192, sig_ref11025 = sig_ref11025,sig_ref44100=sig_ref44100, sig_ref48000=sig_ref48000)\n", "\n", "\n", "# Load the file from the class website\n", "testfile = urllib.URLopener()\n", "testfile.context = ssl._create_unverified_context()\n", "testfile.retrieve(\"https://inst.eecs.berkeley.edu/~ee123/sp16/lab/lab5/debug_ref.npz\", 'debug_ref.npz')\n", "\n", "testnpz = np.load('debug_ref.npz')\n", "\n", "b = bitarray.bitarray()\n", "bits_ref = b.frombytes(testnpz['bits'].tostring())\n", "sig_ref8192 = testnpz['sig_ref8192']\n", "sig_ref11025 = testnpz['sig_ref11025']\n", "sig_ref44100 = testnpz['sig_ref44100']\n", "sig_ref48000 = testnpz['sig_ref48000']\n", "\n", "# Check that all the loaded signals align\n", "fig = figure(figsize(16,4))\n", "plt.plot(r_[0.0:148]/8192,sig_ref8192[:148])\n", "plt.plot(r_[0.0:200]/11025,sig_ref11025[:200])\n", "plt.plot(r_[0.0:800]/44100,sig_ref44100[:800])\n", "plt.plot(r_[0.0:870]/48000,sig_ref48000[:870])\n", "plt.title('AFSK1200 with different sampling rates')\n", "plt.legend(('8192Hz','11024Hz','44100Hz', '48000Hz'))\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Task2 :\n", "\n", "Apply your function on the bitstream above with the different sampling rate. Validate that they match well:\n" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Validation code\n", "\n", "\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Spectrum of AFSK1200\n", "\n", "We will now generate an AFSK modulated signal and compute its spectrum, it should look something like this:\n", "\n", "\n", "![AFSK1200 Spectrum](http://inst.eecs.berkeley.edu/~ee123/sp17/lab/lab5/lab5images/afsk1200spectrum.png)\n", "**Figure 2**\n", "\n", "#### Task 3:\n", "* Generate a new sequence of 4096 random bits with equal probability, using the code `bitarray.bitarray((rand(4096)>0.5).tolist())` \n", "* Generate the AFSK1200 signal at 48KHz \n", "* Compute the average power-spectrum with a spectral resolution of 10Hz (What's the window size?)\n", "* Display the result between 0 and 3KHz, using the command `plt.axis`\n", "* Does the spectrum looks like the one in Figure 2?\n", "\n" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [], "source": [ "fs = 48000\n", "bits=bitarray.bitarray((rand(4096)>0.5).tolist())\n", "\n", "# Your code here\n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## AFSK1200 demodulation\n", "AFSK is a form of digital frequency modulation. As such, it can be demodulated like FM. However, this is not the best way to compute it in practice. For example, any tone interference between the mark and space frequency will break the demodulation. \n", "\n", "Because AFSK alternates between two frequencies, we can place two bandpass filters around the frequency of the Mark and Space and use envelope detection to determine which frequency is active in a bit period. This is called a non-coherent AFSK demodulation, because the receiver phase does not need to be synced to the transmitter phase in order to demodulate the signal. The implementation we will use here is loosly based on on the one by Sivan Toledo (4X6IZ), a CS faculty in Tel-Aviv university who has written a nice article on a high-performance AX.25 modem, and also loosly based DireWolf, a multi-platform software TNC. \n", "\n", "For further optional research, you can find Sivan's article [Here](http://www.cs.tau.ac.il/~stoledo/Bib/Pubs/QEX-JulAug-2012.pdf), and Direwolf GitHub link [Here](https://github.com/wb2osz/direwolf). \n", "\n", "\n", "### Non-Coherent Demodulation of AFSK\n", "Here's a diagram of a non-coherent AFSK1200 demodulator that returns an NRZ signal:\n", "


\n", "
\"AFSK\"
\n", "
\n", "
Figure 3: AFSK1200 non-coherent demodulator
\n", "\n", "\n", "#### Task 4:\n", "\n", "Since our audio device supports 48KHz, we will use this sampling frequency, as it divides well with 1200.\n", "As mentioned in the article, it is recommended to bandpass filter before processing (900-2500Hz). This filter has no theoretical benefit for the case of random gaussian noise. But it still has some advantages when the noise and interferences are non-gaussian. We will not do it now, but will implement this later when we implement our modem.\n", "\n", "* Using signal.firwin, design a TBW=2 LP filter with a (two-sided) bandwidth of approximately 1200Hz (i.e. cutoff of 600 hz). It's easier if you choose the number of taps to be odd. (what's the length of the filter?)\n", "* From the LP filter generate two bandpass filters by complex modulating the LP filter to be centered around 1200Hz and 2200Hz respectively. \n", "* Filter the random stream of bits you generated previously using the two filters. \n", "\n", "The absolute value of the result represents the envelope of the filtered signal. The difference between the envelopes should represent the NRZ signal. \n", "\n", "* Plot the result of the envelope detection of the mark and space signals for the first 4800 samples on the same plot. Can you see how they switch? The result should look something [like this](https://inst.eecs.berkeley.edu/~ee123/sp17/lab/lab5/lab5images/markspacefilteroutput.png)\n", "* Compute the \"analog\" NRZ signal by taking the difference between the mark and space envelopes. I refer to this as \"analog\" since it is not purely binary.\n", "* To implement the optional filter (Fig. 1), filter the NRZ signal, now, with a **low-pass filter**. Have the filter be the same length you used for the mark and spaces, only for its cuttoff frequency, set it to 1200*1.2 Hz. This is a balance between noise rejection and keeping the signal and helps a bit with detection in noise. \n", "* In a different plot, display the filtered NRZ for the first 4800 samples. Can you see the bits?\n", "\n", "\n", "#### \\*\\*\\* Comment: How are parameters fine tuned? \n", "Well... the answer is complicated. There's theory and practice. From a theory point of view, we choose the right bandwidths and signal lengths. From a practical point of view, parameters can be tuned by testing performance over simulations and real-life experiments. For example, WA8IMF created a test CD for testing AFSK1200 modems. He recorded many minutes of APRS traffic in LA. Hardware TNC can usually decode 500-600 packets on these test CD's whereas optimized software TNCs can detect over 1000. Here's a [link](http://wa8lmf.net/TNCtest/) for the CD.\n" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Your code here:\n", "\n", "\n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Task 5:\n", "* Extract the digital NRZ signal by computing the signum (`sign`) function of the \"analog\" NRZ. \n", "* The bit value is the value of the NRZ function in the middle of the bit period. Don't forget to compensate for the delay of the filters, or use `mode='same'` when performing filtering. Decode the bits and store them as a `bitarray` type.\n", "* Plot 800 samples or the Digital NRZ. Overlay a stem plot on top of that at the indexes in which you sampled the bit values. Make sure as a sanity check that you actually sampled at the middle of the interval. Only show the plot for the first 20 bits\n", "* Print the value of the first 100 decoded bits and compared to the encoded ones. Make sure they are exactly the same!\n" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "ename": "NameError", "evalue": "name 'NRZ' is not defined", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 10\u001b[0m \u001b[0;31m# print the decoded bits compared to the transmitted bits\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 12\u001b[0;31m \u001b[0mbit_dec\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbitarray\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbitarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mNRZ\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0midx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m>\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtolist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 13\u001b[0m \u001b[0;32mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbit_dec\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 14\u001b[0m \u001b[0;32mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbits\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mNameError\u001b[0m: name 'NRZ' is not defined" ] } ], "source": [ "# your code here:\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "# print the decoded bits compared to the transmitted bits\n", "\n", "bit_dec = bitarray.bitarray((NRZ[idx]>0).tolist())\n", "print(bit_dec[:100])\n", "print(bits[:100])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Task 6:\n", "\n", "* Write a function NRZ = nc_afsk1200Demod(sig, fs=fs, TBW=TBW) that implements the above non-coherent demodulation and returns the \"analog\" NRZ (i.e. without casting or sampling it). " ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def nc_afsk1200Demod(sig, fs=48000.0, TBW=2.0):\n", " # non-coherent demodulation of afsk1200\n", " # function returns the NRZ (without casting or sampling)\n", " # \n", " # sig - signal\n", " # baud - The bitrate. Default 1200\n", " # fs - sampling rate in Hz\n", " # TBW - TBW product of the filters\n", " #\n", " # Returns:\n", " # NRZ \n", " \n", " \n", " # your code here\n", " \n", " \n", " \n", " \n", " return NRZ\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Bit Error Rate (BER)\n", "One way to evaluate the properties of a digital modulation scheme is to compute the bit-error-rate (BER) curves as a function of signal-to-noise ratio (SNR). The BER is the number of bit errors (received bits that have been altered due to decoding error) divided by the total number of transmitted bits. \n", "\n", "Let's calculate the BER for our AFSK demodulator:\n", "\n", "#### Task 7:\n", "* Generate a 10000 long random bitstream\n", "* AFSK1200 modulate the bitstream\n", "* Add random gaussian noise with a standard deviation of 1 to the afsk signal, using `np.random.randn`\n", "* Demodulate \n", "* Plot the first 2560 samples of the output analog NRZ of the demodulation (64 bits at 48KHz), and overlay a stem plot with stems at the center of bits period -- look at the result. Can you see why digital communication is robust?\n", "* Compute the BER by comparing the bitstream before and after modulation/demodulation\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#Your code here\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "# print the decoded bits compared to the transmitted bits\n", "bit_dec = bitarray.bitarray((NRZ[idx]>0).tolist())\n", "print(bit_dec[:64])\n", "print(bits[:64])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [], "source": [ "# plot code \n", "\n", "\n", "\n", "\n", "\n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Calculate BER\n", "\n", "\n", "\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Your bit error rate should be around 0.0014 will depend also on the quality of the reconstruction. You can try to repeat the experiment for different choices of filters if you like. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Computing BER curves\n", "\n", "BER curves are usually displayed in log log of the BER vs SNR. SNR is measured by energy per bit over noise power spectral density.\n", "Since we are just interested in the trend, we will plot the BER vs 1/noise standard deviation. \n", "\n", "To help you debug your code, we generated our own curves and saved the results. Your results should be similar to ours. \n", "\n", "#### Task 8:\n", "\n", "* Repeat the experiment for the range $\\sigma=[0.1:8.0:0.1]$ \n", "* Use the function loglog to plot the BER as a function of 1/$\\sigma$. What's the BER at really low-SNR? What does it mean about the information the channel is carrying in low-SNR?" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "79\n", "200\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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XwoXwwQ/CyJFw4YU5DEuSpIZiS68kqf8aPBiuvz4vafSf/5nH+0qSJFUx9EqS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\nMdx6K+y3H8ybV3Q1kiQtVS6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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "### Load our simulation result:\n", "# Load the file from the class website\n", "testfile = urllib.URLopener()\n", "testfile.context = ssl._create_unverified_context()\n", "testfile.retrieve(\"https://inst.eecs.berkeley.edu/~ee123/sp16/lab/lab5/BER.npz\", 'BER_ref.npz')\n", "testnpz = np.load('BER_ref.npz')\n", "BER_ref = testnpz['BER']\n", "\n", "\n", "\n", "\n", "# Your code:\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "# plot\n", "\n", "\n", "loglog(1/(r_[0.1:8.0:0.1]),BER_nc)\n", "loglog(1/(r_[0.1:8.0:0.1]),BER_ref[:79],'r')\n", "\n", "plt.legend(('mine','Miki''s'))\n", "\n", "title(\"empirical BER for AFSK demodulation\")\n", "xlabel(\"SNR\")\n", "ylabel(\"BER\")\n", "\n", "\n", "#np.savez_compressed('BER.npz', BER=BER_nc)\n", "\n", "\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Timing Recovery\n", "\n", "One of the most important part of digital demodulation is the synchronization between the transmitter and receiver. We would like to sample the NRZ signal at the peak, which happens in the middle of the bit interval. However, we don't necessarily know when the transmitter starts sending the bits. There could also be some offset with the bit-rate between the transmitter and receiver due to the different clocks in the systems\n", "\n", "There are many ways to do this. For example, if there's a known preamble sequence we could look for, we can perform a match filtering with the known sequence to find the bit rate and synchronize to its start. \n", "\n", "Here we will use a simple, yet elegant solution, that was implemented in DireWolf, which uses a counter based phased-lock-loop (PLL). Here's a system diagram for the timing recovery (D is a sample delay):\n", "\n", "
\"PLL\"
\n", "
Figure 4: Timing Recovery
\n", "\n", "The idea is simple. For each incoming sample we advance a 32bit signed counter by $2^{32}/(f_s/baud)$. The counter will overflow exactly every $fs/baud$ samples. When overflow happens, we record the sign of the NRZ signal and output it. If the counter is synchronized to the NRZ signal, whenever there's a zero-crossing of the NRZ signal due to bit-sign change there also should be a zero-crossing of the counter and the counter should overflow in the middle of a symbol. This is illustrated below:\n", "\n", "
\n", "
\"PLL\"/
\n", "
Figure 5: Counter overlaid on NRZ signal
\n", "\n", "\n", "So, in order to synchronize the PLL counter to the NRZ signal, whenever the NRZ signal has a zero-crossing, we will \"nudge\" the counter by multiplying it with $0\n", "
\"PLL\"/
\n", "
Figure 6: Locking Speed with different values of $a$
\n", "\n", "**For checkoff:** Would we still be able to sample a received signal using our PLL that was transitted too slowly or quickly (i.e. the interval between bits was too large or small)? If the signal is too fast or slow by a factor of $\\beta$, how should $a$ change when $\\beta$ increases?\n", "\n", "\n", "#### Task 9:\n", "\n", "Write a function `idx = PLL(NRZa, a, fs = fs, baud=baud)`. \n", "\n", "The function should take as input a segment of an \"analog\" NRZ sequence, the \"nudge\" factor $a$, the sampling rate and the baud rate. It should return the indexes corresponding to the middle sampling points of symbols.\n", "\n", "* Python types don't allow for overflow, so when incrementing the counter, **cast it to `int32`**, to force overflow. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def PLL(NRZa, a = 0.74 , fs = 48000, baud = 1200):\n", " # \n", " # function implements a simple phased lock loop for tyming recovery\n", " #\n", " # Inputs:\n", " # NRZa - The NRZ signal\n", " # a - nudge factor\n", " # fs - sampling rate (arbitrary)\n", " # baude - the bit rate\n", " #\n", " # Outputs:\n", " # idx - array of indexes to sample at\n", " #\n", " \n", " \n", " # Your code here\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " return idx[]\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The following code generates 4 packets of length 24. Each packet consistes of 1,0,1,0,1,0 training sequence followed by 18 bits. It also puts random spacings between packets as well as noise.\n", "\n", "To help you debug, we generated this packets, modulated, demodulated and computed the timing using our own implementation of the PLL. You can use our data to see if your timings are correct. \n", "\n", "#### Task 10:\n", "* Run your PLL and this data with a=0.75\n", "* Plot the NRZa signal and overlay the sampling points that were returned by the PLL. Make sure the PLL works! You should see that the PLL will lock within very few bit lengths\n", "\n", "** Note that for our data, on the 2nd and 4th packets, the PLL takes 5 bit lengths to lock **\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#bits=bitarray.bitarray([True,False,True,False,True, False]+(np.random.randn(24)>0).tolist())\n", "#sig = zeros(int32(np.random.rand(1)*200+200))\n", "#sig = concatenate((sig,afsk1200(bits, fs=48000)))\n", "\n", "#for n in range(0,3):\n", "# bits=bitarray.bitarray([True,False,True,False, True, False]+(np.random.randn(18)>0).tolist())\n", "# sig = concatenate((sig,zeros(int32(np.random.rand(1)*200+200)),afsk1200(bits, fs=48000)))\n", "#sig = sig + 0.1*np.random.randn(len(sig))\n", "#NRZ = nc_afsk1200Demod(sig, fs=48000, baud =1200)\n", "#idx = PLL(NRZ, a=0.75)\n", "#np.savez_compressed('debug_pll1.npz', idx=idx, sig = sig, bits = bits, NRZ=NRZ)\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#Load the file from the class website\n", "testfile = urllib.URLopener()\n", "testfile.context = ssl._create_unverified_context()\n", "testfile.retrieve(\"https://inst.eecs.berkeley.edu/~ee123/sp16/lab/lab5/debug_pll.npz\", 'debug_pll.npz')\n", "testnpz = np.load('debug_pll1.npz')\n", "bits= testnpz['bits']\n", "sig = testnpz['sig']\n", "idx = testnpz['idx']\n", "NRZ = testnpz['NRZ']\n", "\n", "figure\n", "plot(NRZ)\n", "stem(idx,NRZ[idx.astype(int)])\n", "\n", "\n", "# Run your PLL on the NRZ and compare the idx you get with ours. They should be similar. \n", "\n", "# your code for running your PLL Below here\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Timing Jitter\n", "\n", "Let's see the effect of the PLL nudging factor `a` on the timing jitter. \n", "\n", "#### Task 11:\n", "* Generate a sequence of 1000 random bits\n", "* Add random gaussian noise with standard deviation of 1\n", "* Modulate the signal using AFSK1200\n", "* Add 7 zero samples in the begining so the PLL will need to lock.\n", "\n", "Even though there's noise, the best sampling timing is every 40 samples ( fs=48000 ), because that's the original rate. If our PLL is insensitive to noise, then it should give us the right value all the time.\n", "\n", "* Used the PLL with $a=0.95$, to compute the indices of sampling points. Then, compute the finite difference in the indexes. Ideally, it should be 40 all the time.\n", "* Repeat the above for $a=0.75$, and $a=0.4$\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# You get Task 11 for free!\n", "\n", "\n", "\n", "bits=bitarray.bitarray((np.random.randn(1000)>0).tolist())\n", "sig = afsk1200(bits, fs=48000)\n", "sig = sig + np.random.randn(len(sig))*1\n", "sig=np.concatenate((zeros(10),sig))\n", "NRZ = nc_afsk1200Demod(sig, fs=48000)\n", "idx1 = PLL(NRZ, a=0.9)\n", "idx2 = PLL(NRZ, a=0.75)\n", "idx3 = PLL(NRZ, a=0.4)\n", "\n", "\n", "\n", "fig = plt.figure(figsize=(16,2))\n", "plot(diff(idx1))\n", "title('Jitter for a=0.95')\n", "axis((0,1000,20,50))\n", "fig2 = plt.figure(figsize=(16,2))\n", "plot(diff(idx2))\n", "axis((0,1000,20,50))\n", "title('Jitter for a=0.75')\n", "fig3 = plt.figure(figsize=(16,2))\n", "plot(diff(idx3))\n", "axis((0,1000,20,50))\n", "title('Jitter for a=0.4')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Part 3 will introduce the Data Link Layer." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "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": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 0 }