main.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Aras_Gungore_EE374.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "**Task 1: Pulse Code Modulation**"
      ],
      "metadata": {
        "id": "rhDxXMn9iIWK"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "We would first like to import the relevant libraries (notice the shorthands we define for convenience)"
      ],
      "metadata": {
        "id": "k6zFhT_LiRYY"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 115,
      "metadata": {
        "id": "8dv6oWzmg49a"
      },
      "outputs": [],
      "source": [
        "import math\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Define message signal"
      ],
      "metadata": {
        "id": "bYfTHVJJiYcw"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 116,
      "metadata": {
        "id": "xIa2mHxeuQio"
      },
      "outputs": [],
      "source": [
        "# message signal properties\n",
        "\n",
        "message_frequency_cosine = 100  # in Hertz (Hz)\n",
        "message_frequency_sine = 25     # in Hertz (Hz)\n",
        "message_amplitude_cosine = -1   # (usually) in Volts (V)\n",
        "message_amplitude_sine = 1      # (usually) in Volts (V)\n",
        "\n",
        "\n",
        "# the component with the highest frequency carries the bandwidth\n",
        "\n",
        "bandwidth = max(message_frequency_cosine, message_frequency_sine)   # in Hertz (Hz)\n",
        "sampling_frequency = 2 * bandwidth        # Nyquist sampling frequency in Hertz (Hz)\n",
        "\n",
        "\n",
        "# initialize the time vector\n",
        "\n",
        "signal_duration = 2             # in seconds (s)\n",
        "\n",
        "# sampling starts from t_0 = Ts\n",
        "time = np.arange(start = 1, stop = signal_duration * sampling_frequency) / sampling_frequency\n",
        "\n",
        "\n",
        "# calculate the message signal\n",
        "\n",
        "message_cosine = message_amplitude_cosine * np.cos(2 * np.pi * message_frequency_cosine * time)\n",
        "message_sine = message_amplitude_sine * np.sin(2 * np.pi * message_frequency_sine * time)\n",
        "message = message_cosine + message_sine\n",
        "\n",
        "\n",
        "# calculate the new message signal with higher time sensitivity\n",
        "\n",
        "new_frequency = 1e6\n",
        "new_time = np.arange(start = 1, stop = signal_duration * new_frequency) / new_frequency\n",
        "new_message_cosine = message_amplitude_cosine * np.cos(2 * np.pi * message_frequency_cosine * new_time)\n",
        "new_message_sine = message_amplitude_sine * np.sin(2 * np.pi * message_frequency_sine * new_time)\n",
        "new_message = new_message_cosine + new_message_sine\n",
        "\n",
        "\n",
        "# calculate the negative and positive peak values using new message signal for more accuracy\n",
        "\n",
        "amplitude_pmax = np.amax(new_message)   # find the positive peak amplitude\n",
        "amplitude_nmax = np.amin(new_message)   # find the negative peak amplitude"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Define Pulse Code Modulation (PCM) properties"
      ],
      "metadata": {
        "id": "gKbC1laaxA_X"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "L = 128                           # number of quantization levels\n",
        "no_of_bits = int(math.log2(L))    # number of bits (2^n = L)\n",
        "level_limits = np.linspace(amplitude_pmax, amplitude_nmax, L + 1)     # L + 1 level limits\n",
        "\n",
        "\n",
        "# take averages of the successive level limits to obtain quantization levels\n",
        "\n",
        "quantization_levels = (level_limits[:L] + level_limits[1:]) / 2"
      ],
      "metadata": {
        "id": "c-AWgbgDmm0J"
      },
      "execution_count": 117,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Define the actual modulator"
      ],
      "metadata": {
        "id": "DVY_6rb0z405"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# define the quantized message signal\n",
        "\n",
        "quantized_message = np.zeros_like(message)\n",
        "\n",
        "\n",
        "# go through the modulation loop\n",
        "\n",
        "for i in range(len(message)):\n",
        "  amplitude_diffs = np.abs(message[i] - quantization_levels)\n",
        "\n",
        "  # find the first index where the amplitude difference is greater than or equal to the one before it\n",
        "  # if such index doesn't exist then set the quantized level as L - 1,\n",
        "  # otherwise set the quantized level as the index before the one we have just found\n",
        "  quantized_level = np.where(amplitude_diffs[1:] >= amplitude_diffs[:(L - 1)])[0]\n",
        "  if quantized_level.size == 0:\n",
        "    quantized_message[i] = L - 1\n",
        "  elif quantized_level[0] > 1:\n",
        "    quantized_message[i] = quantized_level[0]"
      ],
      "metadata": {
        "id": "5LhwgcDzwwfq"
      },
      "execution_count": 118,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Display the binary representation of the first 10 samples on the screen"
      ],
      "metadata": {
        "id": "0UHFvjc20LSg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "output = [(np.binary_repr(int(quantized_message[i]), no_of_bits)) for i in range(10)]\n",
        "print(*output, sep = \"-\")"
      ],
      "metadata": {
        "id": "fypMSmNK1EEm",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e5a4ecb1-0893-4e85-8aa7-ff8d3b1eba86"
      },
      "execution_count": 119,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0000000-0111011-0000000-1011101-0110001-1111111-0110001-1011101-0000000-0111011\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Task 2: Delta Modulation**"
      ],
      "metadata": {
        "id": "0Vq4n5TSUxEH"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "We would first like to import the relevant libraries (notice the shorthands we define for convenience)"
      ],
      "metadata": {
        "id": "9VoWYb6SUxEN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "P1wbq86guv6a"
      },
      "execution_count": 120,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Define message signal"
      ],
      "metadata": {
        "id": "FnDhBJw9UxEO"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 121,
      "metadata": {
        "id": "aUbz9X5OUxEO"
      },
      "outputs": [],
      "source": [
        "# initialize the time vector\n",
        "\n",
        "sampling_frequency = 1e6        # in Hertz (Hz)\n",
        "signal_duration = 2             # in seconds (s)\n",
        "time = np.arange(signal_duration * sampling_frequency) / sampling_frequency\n",
        "\n",
        "\n",
        "# message signal properties\n",
        "\n",
        "message_frequency_cosine = 100  # in Hertz (Hz)\n",
        "message_frequency_sine = 25     # in Hertz (Hz)\n",
        "message_amplitude_cosine = -1   # (usually) in Volts (V)\n",
        "message_amplitude_sine = 1      # (usually) in Volts (V)\n",
        "\n",
        "\n",
        "# calculate the message signal\n",
        "\n",
        "message_cosine = message_amplitude_cosine * np.cos(2 * np.pi * message_frequency_cosine * time)\n",
        "message_sine = message_amplitude_sine * np.sin(2 * np.pi * message_frequency_sine * time)\n",
        "message = message_cosine + message_sine"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Define Delta Modulation (DM) properties"
      ],
      "metadata": {
        "id": "MUb3jzxnwXlH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# the component with the highest frequency carries the bandwidth\n",
        "\n",
        "bandwidth = max(message_frequency_cosine, message_frequency_sine)   # in Hertz (Hz)\n",
        "\n",
        "\n",
        "# define delta sampling frequency as 4 times the Nyquist rate\n",
        "\n",
        "nyquist_rate_factor = 4\n",
        "delta_sampling_frequency = nyquist_rate_factor * 2 * bandwidth      # in Hertz (Hz)\n",
        "\n",
        "\n",
        "delta_epsilon = 0.2     # define step size in seconds (s)"
      ],
      "metadata": {
        "id": "BlgG6gOiwXLL"
      },
      "execution_count": 122,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Define the actual modulator"
      ],
      "metadata": {
        "id": "0D9D3OQxwSIc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# define sample time array\n",
        "\n",
        "delta_time = np.arange(signal_duration * delta_sampling_frequency) / delta_sampling_frequency\n",
        "\n",
        "\n",
        "# calculate the sampled message signal\n",
        "\n",
        "sampled_message_cosine = message_amplitude_cosine * np.cos(2 * np.pi * message_frequency_cosine * delta_time)\n",
        "sampled_message_sine = message_amplitude_sine * np.sin(2 * np.pi * message_frequency_sine * delta_time)\n",
        "sampled_message = sampled_message_cosine + sampled_message_sine\n",
        "\n",
        "\n",
        "# allocate memory\n",
        "\n",
        "prediction = np.zeros((len(sampled_message), 1))\n",
        "modulated = np.zeros_like(prediction)\n",
        "\n",
        "\n",
        "# go through the modulation loop\n",
        "\n",
        "for i, s_amplitude in enumerate(sampled_message[1:]):\n",
        "  amplitude_diff = s_amplitude - prediction[i]\n",
        "  modulated[i + 1] = (2 * int(amplitude_diff > 0) - 1) * delta_epsilon\n",
        "  prediction[i + 1] = prediction[i] + modulated[i + 1]"
      ],
      "metadata": {
        "id": "VSpU0MoMwRBl"
      },
      "execution_count": 123,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Print the first 20 samples on the screen"
      ],
      "metadata": {
        "id": "i25u9t-UsQ-N"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "output = [int(modulated[i] > 0) for i in range(1, 21)]\n",
        "print(*output, sep=\"-\")"
      ],
      "metadata": {
        "id": "DF1OzBri0jx6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e817fe7c-60a1-4bc6-de41-39634af9ce8e"
      },
      "execution_count": 124,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0-1-1-1-1-1-0-0-0-1-1-1-1-0-0-0-0-0-1-1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Plot obtained signals (optional)"
      ],
      "metadata": {
        "id": "ZsQXtEa7sK3j"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# define plot parameters\n",
        "\n",
        "plt.figure(figsize = (20, 12))\n",
        "plot_time = 0.1\n",
        "\n",
        "\n",
        "# plot the message and predicted signals\n",
        "\n",
        "plt.subplot(2, 1, 1)\n",
        "plt.plot(time, message, 'b')\n",
        "# plt.step(delta_time, sampled_message, 'c--', where = 'post')\n",
        "plt.step(delta_time, prediction, 'r', where = 'post')\n",
        "plt.xlabel('Time (s)')\n",
        "plt.ylabel('Amplitude')\n",
        "plt.title('Message and Predicted Signals')\n",
        "plt.legend(['Message signal', 'Predicted signal'])\n",
        "plt.axis([0, plot_time, -2, 2])\n",
        "plt.xticks(np.arange(0, plot_time, plot_time/10))\n",
        "plt.grid()\n",
        "\n",
        "\n",
        "# plot the transmitted DM signal\n",
        "\n",
        "plt.subplot(2, 1, 2)\n",
        "plt.stem(delta_time, modulated, 'b')\n",
        "plt.xlabel('Time (s)')\n",
        "plt.ylabel('Amplitude')\n",
        "plt.title('Transmitted DM Signal')\n",
        "plt.axis([0, plot_time, -2*delta_epsilon, 2*delta_epsilon])\n",
        "plt.xticks(np.arange(0, plot_time, plot_time/10))\n",
        "plt.grid()"
      ],
      "metadata": {
        "id": "dsFqlKtwv1tY",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 503
        },
        "outputId": "a00ce35a-c2c6-4246-f856-ae34957d863a"
      },
      "execution_count": 125,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:25: UserWarning: In Matplotlib 3.3 individual lines on a stem plot will be added as a LineCollection instead of individual lines. This significantly improves the performance of a stem plot. To remove this warning and switch to the new behaviour, set the \"use_line_collection\" keyword argument to True.\n",
            "/usr/local/lib/python3.7/dist-packages/numpy/core/shape_base.py:65: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
            "  ary = asanyarray(ary)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x864 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}