A.K.03/Manual/4_Module_2.ipynb
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"![book header](pictures/header.png)\n",
"\n",
"[Table of Contents](0_Table_of_Contents.ipynb)\n",
"\n",
"# Chapter 4: Module 2 - Reading KITT Sensor Data\n",
"\n",
"**Contents:**\n",
"* [Distance Sensor](#distance-sensors)\n",
"* [The Microphones](#the-microphones)\n",
"* [FAQ](#faq)\n",
"\n"
]
},
{
"cell_type": "code",
"metadata": {
"ExecuteTime": {
"end_time": "2025-11-28T08:43:34.403324Z",
"start_time": "2025-11-28T08:43:33.090082Z"
}
},
"source": [
"# Import necessary libraries\n",
"import time\n",
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import csv\n",
"\n",
"# Uncomment one of the following lines depending on your setup\n",
"\n",
"# If you are using the real car, uncomment the next lines and comment the simulator lines\n",
"from serial import Serial\n",
"import sounddevice\n",
"\n",
"# If you are using the simulator, uncomment the next lines and comment the real car lines\n",
"# from KITT_Simulator.serial_simulator import Serial\n",
"# from KITT_Simulator.sounddevice_simulator import sounddevice\n",
"\n",
"# Note: After changing the import statement, you need to restart the kernel for changes to take effect."
],
"outputs": [],
"execution_count": 1
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"KITT relies on its sensors to drive autonomously. It is equipped with:\n",
"1. Two front-mounted distance sensors.\n",
"2. Five microphones positioned around the field to record audio signals from KITT's beacon and relay them to the soundcard, after which they can be read by your PC.\n",
"\n",
"This task focuses on reading data from the distance sensors to avoid obstacles and processing the microphone data from the field.\n",
"\n",
"**Preparation**\n",
"- Ensure KITT is operational and properly set up.\n",
"- Reserve a time slot for testing on a field equipped with microphones and an audio card.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Distance Sensors\n",
"\n",
"KITTs front distance sensors use ultrasonic technology. Two SRF02 modules, mounted on the left and right sides, measure the distance to obstacles. These \"parking sensors\" work by emitting a 40 kHz pulse and measuring the time it takes for the echo to return. This time is converted into a distance measurement.\n",
"\n",
"- Each sensor requires a minimum of 66 ms between readings, as specified in the SRF02 datasheet (available on Brightspace or at `Files/Datasheets/srf02.pdf`).\n",
"- The system is configured with a 70 ms cycle time; the left and right sensors take turns recording measurements.\n",
"- These measurements are stored in a buffer on KITT's microcontroller, with each new reading overwriting the previous one. When you request the KITT status, you will obtain a copy of the current buffer values.\n",
"\n",
"<img src=\"pictures/srf02-ultrasonic-sensor.jpg\" alt=\"Ultrasonic Sensor\" width=\"400\" height=\"240\">\n",
"\n",
"### Step 0: Characteristics of the Distance Sensors\n",
"\n",
"Using the readings on the car display, report on the following questions:\n",
"\n",
"1. What is the accuracy of the distance sensors? Does this change with distance?\n",
"2. What are the minimum and maximum distances the sensors can measure?\n",
"3. What is the field of view of the distance sensors (beam angle)?\n",
"\n",
"To measure this field of view, move an obstacle from left to right over a line, at about 1 m distance from the sensors, and observe when the sensors start to 'see' the object.\n",
"\n",
"The field of view is important when making recordings: you should realize that the distance sensors may detect chairs, bags, etc., and then make false readings. This happens even if these objects are not straight in front of the sensors. (The field of view does depend on distance.)\n",
"\n",
"**Note:** Do not copy the questions into your report, but naturally include the information in your report as part of your discussion.\n",
"\n",
"### Step 1: Status Command\n",
"\n",
"To ensure you can experiment at home, we have added the status command to the simulator. The simulator will accurately simulate the sensor distances, but not its behavior. Make sure to test on the real car frequently.\n",
"\n",
"As you have learned in the previous module, you can ask KITT to capture a status command by writing `\"S\\n\"` to the serial port. Then you have to read the message using `read_until`; this will generate a binary message that you need to decode. KITT always ends its message with the end-of-transmission character (0x04). The response contains three sections:\n",
"1. Audio beacon status and settings\n",
"2. PWM values for the motors\n",
"3. Sensor readings"
]
},
{
"cell_type": "code",
"metadata": {
"ExecuteTime": {
"end_time": "2025-11-21T08:40:49.777160Z",
"start_time": "2025-11-21T08:40:48.574633Z"
}
},
"source": [
"### Student Version ###\n",
"\n",
"serial = Serial('COM4', 115200)\n",
"serial.write(b'Sd\\n')\n",
"status = serial.read_until(b\"\\x04\")\n",
"status = status.decode('utf-8')\n",
"print(f\"Car status is:\\n\\n{status}\")\n",
"\n",
"serial.close()"
],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Car status is:\n",
"\n",
"USL99\n",
"USR51\n",
"\u0004\n"
]
}
],
"execution_count": 45
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-11-21T08:52:18.034321Z",
"start_time": "2025-11-21T08:52:18.022808Z"
}
},
"cell_type": "code",
"source": "serial.close()",
"outputs": [],
"execution_count": 51
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If you only need distance measurement information, you can request it separately:\n",
"\n",
"```python\n",
"serial.write(b'Sd\\n')\n",
"```\n",
"\n",
"This returns only the left and right distance sensor values, filtering out the rest of the status report.\n",
"\n",
"### Step 2: Extracting and Isolating Distance Data\n",
"\n",
"Assuming you have received the full status information from KITT, you can extract and isolate the distance sensor readings (left and right) from the status report.\n",
"\n",
"After sending the status command (`b'S\\n'`), the response will contain a variety of information, including the distance measurements. Now write a Python function to extract the distance data from the status report.\n",
"\n",
"1. **Extract the distance measurements**:\n",
"\n",
"The distance values are typically embedded in the `Sensors` section of the status response. You can process the `status` output to isolate just the left (`L`) and right (`R`) distance sensor values. Write a function to extract these values.\n",
"\n",
"*Hints:*\n",
"\n",
"- Use `decode('utf-8')` to convert bytes to a string.\n",
"- Use `splitlines()` to separate the status message into individual lines.\n",
"- Look for the line that contains `\"Dist.\"` to find the distance measurements.\n",
"- Use `split()` to break the line into individual words.\n",
"- Be cautious of the positions of the distance values in the list; adjust indices as necessary.\n",
"\n",
"**Note:** If you use the `Sd` status command, you retrieve less info and can write a faster function! The parsing of the status string is also easier."
]
},
{
"cell_type": "code",
"metadata": {
"ExecuteTime": {
"end_time": "2025-11-21T08:49:45.232118Z",
"start_time": "2025-11-21T08:49:45.222261Z"
}
},
"source": [
"### Student Version ###\n",
"\n",
"def extract_dis ():\n",
" serial = Serial('COM4', 115200)\n",
" serial.write(b'S\\n')\n",
" _status = serial.read_until(b'\\x04')\n",
" _status = _status.decode('utf-8')\n",
"\n",
" lines = _status.splitlines()\n",
"\n",
" # Initialize variables to hold distance values\n",
" dist_L = None\n",
" dist_R = None\n",
"\n",
" # Iterate over each line to find distance data\n",
" for line in lines:\n",
" if \"Dist.\" in line:\n",
" words = line.split()\n",
" # Extract distance values based on their positions\n",
"\n",
" # Assign dist_L and dist_R accordingly\n",
" dist_L = words[3]\n",
" dist_R = words[5]\n",
" break # Exit the loop after finding the distances\n",
"\n",
" # Print the extracted distance values\n",
" print(f\"Left Distance: {dist_L}\")\n",
" print(f\"Right Distance: {dist_R}\")\n",
"\n",
" serial.close()\n",
"\n",
" return dist_L, dist_R"
],
"outputs": [],
"execution_count": 46
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-11-21T08:38:36.110680Z",
"start_time": "2025-11-21T08:38:34.392005Z"
}
},
"cell_type": "code",
"source": "extract_dis()",
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Left Distance: 99\n",
"Right Distance: 51\n"
]
},
{
"data": {
"text/plain": [
"('99', '51')"
]
},
"execution_count": 44,
"metadata": {},
"output_type": "execute_result"
}
],
"execution_count": 44
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"2. **Determine how fast you can read out (and process) your distance data** by writing a script that requests the status 100 times. You can calculate the average delay for this operation (and its standard deviation); you could also present the results in a histogram. To measure delays, you will need to keep track of time:\n",
"\n",
"```python\n",
"start_time = time.time() # Initialize\n",
"current_time = time.time() - start_time # Find current time since initialization\n",
"```\n",
"\n",
"If you can read out the sensors faster than 70 ms (or is it 140 ms?), reason if you will obtain duplicate values from the buffer.\n",
"\n",
"**Student Task:**\n",
"\n",
"- Write a script that sends the status command 100 times, recording the time taken for each read.\n",
"- Store the time intervals in a list.\n",
"- After collecting the data, calculate the average delay and standard deviation.\n",
"- Plot a histogram of the delays."
]
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-11-21T08:54:21.237164Z",
"start_time": "2025-11-21T08:54:21.222343Z"
}
},
"cell_type": "code",
"source": "serial.close()",
"outputs": [],
"execution_count": 55
},
{
"cell_type": "code",
"metadata": {
"ExecuteTime": {
"end_time": "2025-11-21T09:22:30.754889Z",
"start_time": "2025-11-21T09:22:21.720051Z"
}
},
"source": [
"### Student Version ###\n",
"times = []\n",
"new_times = []\n",
"total_time = 0\n",
"summed_times = 0\n",
"serial = Serial('COM4', 115200)\n",
"\n",
"for i in range(100):\n",
" start_time = time.time()\n",
" serial.write(b'S\\n')\n",
" _status = serial.read_until(b'\\x04')\n",
" _status = _status.decode('utf-8')\n",
" current_time = time.time() - start_time\n",
" times.append(current_time)\n",
"\n",
"serial.close()\n",
"#print(times)\n",
"\n",
"for j in range(100):\n",
" total_time += times[j]\n",
"\n",
"average = total_time / 100\n",
"\n",
"for k in range(100):\n",
" new_times.append((times[k] - average) ** 2)\n",
" summed_times += new_times[k]\n",
"\n",
"variance = summed_times / 100\n",
"standard_deviation = (variance) ** 0.5\n",
"\n",
"print(f'The average time of a distance measurement is: {average:.3f} [s]')\n",
"print(f'The standard deviantion of a distance measurement is: {standard_deviation:.3f} [s]')\n",
"\n",
"plt.hist(times)\n",
"plt.show()"
],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The average time of a distance measurement is: 0.079 [s]\n",
"The standard deviantion of a distance measurement is: 0.035 [s]\n"
]
},
{
"data": {
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
],
"image/png": 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"
},
"metadata": {},
"output_type": "display_data",
"jetTransient": {
"display_id": null
}
}
],
"execution_count": 63
},
{
"metadata": {},
"cell_type": "markdown",
"source": "<font color=#6698FF> As the ultrasonic sensors refresh every 70 ms taking turns, every 140 ms will give new data. So we need to measure every 140 ms to get new data and not data stored in the buffer."
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Step 3: Using Distance Values to Model the Car\n",
"\n",
"Ultrasonic sensors are not just used for detecting obstacles; they play a crucial role in modeling the car's behavior during autonomous driving. To control the car effectively, we need to understand how it responds to drive and steering commands, similar to how a human driver knows how much acceleration or steering input affects the car's movement.\n",
"\n",
"However, while KITT doesnt have an accelerometer to measure acceleration directly, we can use the ultrasonic sensors to estimate how the car moves over time. By measuring the distance to a cardboard-box wall, we can derive its speed and acceleration.\n",
"\n",
"#### Understanding Speed and Acceleration\n",
"\n",
"- **Velocity** is the rate of change of position over time:\n",
"\n",
" $$\n",
" v(t) = \\frac{{\\mathrm d} x}{{\\mathrm d} t}(t) \\,.\n",
" $$\n",
"\n",
"- **Acceleration** is the change in speed over time:\n",
"\n",
" $$\n",
" a(t) = \\frac{{\\mathrm d} v}{{\\mathrm d} t}(t) \\,.\n",
" $$\n",
"\n",
"Note that $x(t)$, $v(t)$ and $a(t)$ are time varying. To implement the differentials, in practice we will subtract two subsequent samples $x(t_1)$ and $x(t_2)$. We will then have an estimate of $v(t)$ for $t = (t_1+t_2)/2$:\n",
"\n",
"$$\n",
"v\\left(\\frac{t_1+t_2}{2}\\right) \\approx \\frac{x(t_2) - x(t_1)}{t_2-t_1} \\,.\n",
"$$\n",
"\n",
"In theory, this approximation gets better for $t_2$ close to $t_1$, but at the same time the division of two small numbers will make the result very sensitive to noise, so in practice there is a trade-off.\n",
"\n",
"#### Plotting KITT's Motion Towards a Wall\n",
"\n",
"To understand how KITT moves, make recordings of the distance sensor values as KITT drives towards a wall. Do this for multiple motor commands, and store them in a `.csv` file. (You can use the `Files/Recordings` folder to organize your data). You can then later import the data into Python. Next to the sensor values, you should also store the time stamp of each sample.\n",
"\n",
"You will see KITT speed up, and then reach a constant speed. To do this experiment, please let KITT drive towards the supplied cardboard wall. **Turn off KITT's motors once the distance is less than 40 cm to ensure KITT does not crash into the wall.** Note that you may have to discard the first few readings as they may be inaccurate.\n",
"\n",
"*Hints:*\n",
"\n",
"- Choose an appropriate motor speed value for `motor_speed_value`.\n",
"- Ensure that you stop the car if it gets too close to the wall to prevent collisions.\n",
"- Use `time.time()` to keep track of elapsed time.\n",
"- Store the data in a list with the format `[current_time, dist_L, dist_R]`.\n",
"- Write the data to a CSV file for later analysis.\n",
"- Also document the motor speed setting (e.g. use this as part of your file name)."
]
},
{
"cell_type": "code",
"metadata": {
"ExecuteTime": {
"end_time": "2025-11-21T11:13:56.871532Z",
"start_time": "2025-11-21T11:13:52.882260Z"
}
},
"source": [
"### Student Version ###\n",
"from pathlib import Path\n",
"\n",
"serial = Serial('/dev/rfcomm3', 115200)\n",
"serial.write(b'D150\\n')\n",
"serial.write(b'M165\\n')\n",
"\n",
"# Initialize a list to store recorded data\n",
"data = []\n",
"\n",
"# Record data for a specified duration (e.g., 10 seconds)\n",
"recording_duration = 10 # in seconds\n",
"start_time = time.time()\n",
"\n",
"while time.time() - start_time < recording_duration:\n",
" # Send the status command to get the distance readings\n",
" serial.write(b'S\\n')\n",
" \n",
" # Read the status response\n",
" status = serial.read_until(b'\\x04').decode('utf-8')\n",
" \n",
" dist_L = None\n",
" dist_R = None\n",
"\n",
" lines = status.splitlines()\n",
"\n",
" for line in lines:\n",
" if \"Dist.\" in line:\n",
" words = line.split()\n",
" # Extract distance values based on their positions\n",
"\n",
" # Assign dist_L and dist_R accordingly\n",
" dist_L = int(words[3])\n",
" dist_R = int(words[5])\n",
" break\n",
"\n",
" current_time = time.time() - start_time\n",
" data.append([current_time, dist_L, dist_R])\n",
" \n",
" # Check if KITT is too close to the wall and stop if necessary\n",
" if dist_L < 130 or dist_R < 130:\n",
" serial.write(b'M135\\n')\n",
" time.sleep(0.75)\n",
" serial.write(b'M150\\n') # Stop the car\n",
" print(\"Stopping KITT to avoid collision.\")\n",
" break # Exit the loop\n",
" # Note: you can also add a small loop here and still read the stopping data\n",
" \n",
" time.sleep(0.1) # Wait before the next reading\n",
"\n",
"# Close the serial connection\n",
"serial.close()\n",
"\n",
"filepath = Path('./kitt_wall_data_165.csv')\n",
"df = pd.DataFrame(data,columns = [\"Time\",\"Distance_L\",\"Distance_R\"])\n",
"df.to_csv(filepath,index=False)\n",
"!pwd\n",
"# Recommeded file output: Files/Recordings/kitt_distance_data_{speed}.csv"
],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Stopping KITT to avoid collision.\n",
"/home/nano/Documents/EE/Y2/IP3/A.K.03/Manual\r\n"
]
}
],
"execution_count": 17
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-11-21T11:12:41.634004Z",
"start_time": "2025-11-21T11:12:41.624576Z"
}
},
"cell_type": "code",
"source": "df.to_csv(Path(\"./kitt_wall_data_165.csv\"))",
"outputs": [],
"execution_count": 16
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Processing the Recorded Data\n",
"\n",
"Read your `.csv` data into Python (see template script below), and plot the distance values over time to visualize KITT's motion. Use a single plot with separate colors for the L and R sensors. You should notice a 'staircase' shape! Explain this in your report.\n",
"\n",
"Next, to derive velocity, first merge the L and R sensor data into a single position estimate: remove the duplicate values (keep only the first value of a duplicate reading), then merge the remaining values into a single (time, position) array. Plot the result in your distance plot to see if you did this correctly.\n",
"\n",
"After that, estimate the velocity of KITT as function of time. Obviously, you will use $ v(t) = \\Delta x / \\Delta t \\ $, but what time $t$ do you associate with each of these estimates?\n",
"\n",
"Make a plot of the resulting velocity estimates over time.\n",
"\n",
"*Hints:*\n",
"\n",
"- When merging the L and R distance measurements, don't simply average them. Read the above paragraph again.\n",
"- Be aware that the sensors alternate readings every 70 ms, leading to a 'staircase' effect.\n",
"- To calculate velocity, use the differences in distance and time (`diff()` function).\n",
"- Since KITT is moving towards the wall, the distance decreases; yet, the estimated velocity should be positive when moving forward.\n",
"- For the time associated with each velocity estimate, use the midpoint between consecutive time stamps.\n",
"- Remove any NaN values resulting from the `diff()` operation."
]
},
{
"cell_type": "code",
"metadata": {
"ExecuteTime": {
"end_time": "2025-11-26T14:37:36.226400Z",
"start_time": "2025-11-26T14:37:33.263943Z"
}
},
"source": [
"### Student Version ###\n",
"\n",
"motor_speed_value = 160 # Use the same motor speed as during recording\n",
"\n",
"# Load the recorded data from the CSV file\n",
"csv_filename = f'./kitt_wall_data_160.csv'\n",
"data = pd.read_csv(csv_filename)\n",
"\n",
"# Consider discarding the first few readings (inaccurate readings)\n",
"data = data[2:]\n",
"\n",
"# Create a new DataFrame to hold your processed data\n",
"merged_data = []\n",
"plotting_data = []\n",
"\n",
"# Iterate over the data\n",
"for index, row in data.iterrows():\n",
" # Extract time and distances\n",
" time_stamp = row['Time']\n",
" dist_L = row['Distance_L']\n",
" dist_R = row['Distance_R']\n",
" \n",
" distance = min(dist_L,dist_R)\n",
"\n",
" merged_data.append([time_stamp, distance])\n",
" plotting_data.append([time_stamp, dist_L, dist_R])\n",
"\n",
"# Convert merged data to DataFrame\n",
"merged_df = pd.DataFrame(merged_data, columns=['Time', 'Distance'])\n",
"plotting_df = pd.DataFrame(plotting_data, columns=['Time', 'Dist_L', 'Dist_R'])\n",
"\n",
"# Plotting the distance measured by the left and the right sensor\n",
"plt.figure()\n",
"plt.plot(plotting_df['Time'], plotting_df['Dist_L'], color='blue', label='Left sensor')\n",
"plt.plot(plotting_df['Time'], plotting_df['Dist_R'], color='red', label='Right sensor')\n",
"plt.xlabel('Time (s)')\n",
"plt.ylabel('Distance (cm)')\n",
"plt.title('Distance from the left and right sensor')\n",
"plt.grid(True)\n",
"plt.legend()\n",
"plt.show()\n",
"\n",
"merged_df['Velocity'] = -merged_df['Distance'].diff().div(merged_df['Time'].diff())\n",
"\n",
"# Note: Use a negative sign because distance to the wall decreases as KITT moves forward\n",
"\n",
"# Calculate the time corresponding to each velocity estimate\n",
"# It's common to use the midpoint of the time intervals\n",
"merged_df['Velocity_Time'] = merged_df['Time'] - merged_df['Time'].diff() / 2\n",
"\n",
"# Plotting Distance\n",
"plt.figure()\n",
"plt.plot(merged_df['Time'], merged_df['Distance'], label='Distance to Wall')\n",
"plt.xlabel('Time (s)')\n",
"plt.ylabel('Distance (cm)')\n",
"plt.title('Distance to Wall Over Time')\n",
"plt.grid(True)\n",
"plt.legend()\n",
"plt.show()\n",
"\n",
"# Plotting Velocity\n",
"plt.figure()\n",
"plt.plot(merged_df['Velocity_Time'], merged_df['Velocity'], label='Velocity (cm/s)')\n",
"plt.xlabel('Time (s)')\n",
"plt.ylabel('Velocity (cm/s)')\n",
"plt.title('Velocity of KITT Over Time')\n",
"plt.grid(True)\n",
"plt.legend()\n",
"plt.show()\n",
"\n",
"print(merged_df)"
],
"outputs": [
{
"data": {
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
],
"image/png": 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"
},
"metadata": {},
"output_type": "display_data",
"jetTransient": {
"display_id": null
}
},
{
"data": {
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
],
"image/png": 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"
},
"metadata": {},
"output_type": "display_data",
"jetTransient": {
"display_id": null
}
},
{
"data": {
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
],
"image/png": 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"
},
"metadata": {},
"output_type": "display_data",
"jetTransient": {
"display_id": null
}
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" Time Distance Velocity Velocity_Time\n",
"0 0.666117 291.0 NaN NaN\n",
"1 0.837830 288.0 17.471025 0.751974\n",
"2 1.030023 283.0 26.015511 0.933927\n",
"3 1.207749 276.0 39.386623 1.118886\n",
"4 1.390952 267.0 49.125710 1.299350\n",
"5 1.591268 257.0 49.921077 1.491110\n",
"6 1.790691 253.0 20.057907 1.690980\n",
"7 1.991666 240.0 64.684758 1.891178\n",
"8 2.179857 226.0 74.392436 2.085761\n",
"9 2.338029 213.0 82.188818 2.258943\n",
"10 2.515954 198.0 84.305133 2.426992\n",
"11 2.714974 190.0 40.196935 2.615464\n",
"12 2.902802 174.0 85.184397 2.808888\n",
"13 3.096991 157.0 87.543546 2.999897\n",
"14 3.247902 140.0 112.649286 3.172447\n",
"15 3.460058 122.0 84.843396 3.353980\n",
"16 3.627290 103.0 113.614594 3.543674\n",
"17 3.832908 84.0 92.404083 3.730099\n"
]
}
],
"execution_count": 40
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Tips to Consider:**\n",
"\n",
"**Continuous Measurement** involves data that can be taken at any point in time, with no gaps. For example, a cars speedometer provides a continuous record of the cars speed.\n",
"\n",
"**Discrete Measurement**, on the other hand, collects data at specific intervals. For instance, KITTs ultrasonic sensors take distance readings every 70 ms. In between these measurements, we dont know the exact position of the car. Discrete data can still be useful, but it may miss details about rapid changes in speed or acceleration that occur between measurements. In order to interpret it correctly, you may need to filter or interpolate the data.\n",
"\n",
"The following shows the difference between continuous and discrete data:"
]
},
{
"cell_type": "code",
"metadata": {
"ExecuteTime": {
"end_time": "2025-11-26T13:55:32.596911Z",
"start_time": "2025-11-26T13:55:31.928370Z"
}
},
"source": [
"# Define time for continuous measurement (smooth, no gaps)\n",
"time_continuous = np.linspace(0, 10, 1000) # Time from 0 to 10 seconds, 1000 data points\n",
"# Define time for discrete measurement (specific intervals)\n",
"time_discrete = np.linspace(0, 10, 20) # Time from 0 to 10 seconds, 20 data points\n",
"# Simulate continuous speed (sinusoidal speed pattern for illustration)\n",
"speed_continuous = 10 * np.sin(0.5 * np.pi * time_continuous) # Continuous speed\n",
"# Simulate discrete speed (sampled at specific intervals)\n",
"speed_discrete = 10 * np.sin(0.5 * np.pi * time_discrete) # Discrete speed\n",
"# Plotting both continuous and discrete measurements\n",
"plt.figure(figsize=(7, 4))\n",
"plt.plot(time_continuous, speed_continuous, label=\"Continuous Measurement\", color=\"blue\")\n",
"plt.scatter(time_discrete, speed_discrete, label=\"Discrete Measurement\", color=\"red\", zorder=5)\n",
"plt.xlabel('Time (s)')\n",
"plt.ylabel('Speed (cm/s)')\n",
"plt.title('Continuous vs Discrete Measurement of Speed')\n",
"plt.legend()\n",
"plt.grid(True)\n",
"plt.show()"
],
"outputs": [
{
"data": {
"text/plain": [
"<Figure size 700x400 with 1 Axes>"
],
"image/png": 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"
},
"metadata": {},
"output_type": "display_data",
"jetTransient": {
"display_id": null
}
}
],
"execution_count": 38
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Step 5: Implementing the Distance Sensor Reading in Your KITT Class\n",
"\n",
"In the previous module, you have created a class for KITT. Add a method to read the distance sensors to your `KITT` class in your 'Student Code' files. You can use the code you have written in the previous steps to do this. Make sure to test your code. It is advisable to store all the old distance data in a list inside the `KITT` class. This will be convenient during the final challenge, where the route planning might need old measurements to determine the position of objects.\n",
"\n",
"**Student Task:**\n",
"\n",
"- Add a method `read_distance_sensors()` to your `KITT` class.\n",
"- The method should send the status command to KITT and extract the distance measurements.\n",
"- Store the readings along with timestamps in an internal list or data structure.\n",
"\n",
"### Step 6: Mid-term Assessment 2.1 and Report\n",
"\n",
"After you finish this assignment, and ultimately in week 4, showcase the functionality of your script to your assigned TA. After you pass this assessment, you are ready to document your results in your midterm report. For this Module, you would include a chapter that covers the above tasks (using independently-readable text, i.e., dont refer to “Step 1”).\n",
"\n",
"Include plots; for each plot, it should be clear how the plot was made (i.e., the corresponding experimental setup), and you have to describe what is seen in the plot before you discuss results and derive conclusions. Review the guidelines in Chapter 7 for more information. Include the corresponding code in an appendix.\n",
"\n",
"Remember to document your code, using comments to define input/output variables of functions and to explain the logic and any modifications made. Your completed script will be crucial for the upcoming challenges, contributing to the overall autonomous driving system."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## The Microphones\n",
"\n",
"The field is equipped with four microphones at its corners, and a fifth microphone positioned at a higher level between two of the edge microphones. These microphones, together with the beacon mounted on KITT, will be used to locate KITT within the field (more details in Chapter 5).\n",
"\n",
"<img src=\"pictures/axisdef.png\" alt=\"Microphones Axis Defenition\" width=\"400\" height=\"240\">\n",
"\n",
"To use the microphone array, you must ensure that the correct sound card driver is installed. The sound card used in this project is the **Scarlett 18i20 3rd Gen**. Below are instructions on how to configure Sounddevice and the necessary drivers on different platforms.\n",
"\n",
"### Simulator\n",
"For the Sounddevice package another simulator has been made. The simulator will return a realistic audio recording, and change the recordings according to the location of the car. But, it contains only 1 recording, so it will not appear as random as the real car. It also does not adjust to your particular beacon settings. Make sure to test on the real car frequently. Use it in combination with the serial simulator to change locations and test like you would on the real car.\n",
"\n",
"### Important: Lab rules for the microphone array\n",
"\n",
"When working with the microphone array, please follow these rules to ensure smooth operations and avoid disrupting other groups:\n",
"\n",
"1. **Do not rearrange the microphone connectors**. The setup is shared between multiple groups, and changing the connections may lead to incorrect results for other teams.\n",
"2. **Do not touch the volume settings**. If the volume needs adjustment, contact a TA for assistance.\n",
"3. **Handle the equipment carefully**. The microphone array and associated hardware are sensitive, and mishandling could cause damage.\n",
"4. **Start and stop on time**. The lab is shared, and other groups have scheduled time slots. Be respectful of their time.\n",
"\n",
"Test time is limited. But by using the simulator and preparing a plan of what you want to test during each scheduled slot, there should be enough time to complete the tasks. \n",
"\n",
"### Step 1: Initializing the microphone array\n",
"\n",
"Before using the microphone array, it must first be initialized. As part of the initialization process, you will need to specify the sampling frequency (`Fs`) that will be used to record the audio. The sampling frequency will vary based on the test field you are working with, and it will be **48 kHz** or **44.1 kHz**.\n",
"\n",
"A typical laptop or PC may have multiple audio devices (e.g., built-in microphones, Bluetooth headsets, external sound cards). To ensure that the correct device is used, you can list all available audio devices using Sounddevice and select the appropriate one. Use the following code snippet to list all audio devices recognized by Sounddevice and find the index of the Scarlett 18i20 or any other relevant device:"
]
},
{
"cell_type": "code",
"metadata": {
"ExecuteTime": {
"end_time": "2025-11-28T09:27:31.162410Z",
"start_time": "2025-11-28T09:27:31.159715Z"
}
},
"source": [
"for i, device in enumerate(sounddevice.query_devices()):\n",
" print(i, device['name'])\n"
],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0 HDA Intel PCH: ALC3204 Analog (hw:0,0)\n",
"1 HDA Intel PCH: HDMI 0 (hw:0,3)\n",
"2 HDA Intel PCH: HDMI 1 (hw:0,7)\n",
"3 HDA Intel PCH: HDMI 2 (hw:0,8)\n",
"4 HDA Intel PCH: HDMI 3 (hw:0,9)\n",
"5 Scarlett 18i20 USB: Audio (hw:1,0)\n",
"6 sysdefault\n",
"7 front\n",
"8 surround40\n",
"9 surround51\n",
"10 surround71\n",
"11 hdmi\n",
"12 pipewire\n",
"13 dmix\n",
"14 default\n",
"15 Scarlett 18i20 3rd Gen Direct Scarlett 18i20 USB\n",
"16 Built-in Audio Analog Stereo\n"
]
}
],
"execution_count": 34
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Once you have identified the index of the microphone array device from the list, you can initialize it by specifying the device index (`device_index`) and the desired sampling frequency (`Fs`).\n",
"\n",
"Heres how you can open the audio stream using Sounddevice:"
]
},
{
"cell_type": "code",
"metadata": {
"ExecuteTime": {
"end_time": "2025-11-28T09:27:15.411313Z",
"start_time": "2025-11-28T09:27:15.360694Z"
}
},
"source": [
"import sounddevice as sd\n",
"\n",
"#device_index = 15 # your input device index\n",
"#Fs = 48000 # sample rate\n",
"\n",
"# Open 5channel float32 input stream on that device\n",
"#stream = sd.InputStream(\n",
"# device=device_index, # or device=(device_index, None) for in/out\n",
"# channels=5,\n",
"# samplerate=Fs,\n",
"# dtype='float32', # 16bit would be 'int16'\n",
" # or True, depending on how you use it\n",
"#)\n",
"\n",
"#stream.start()\n",
"#stream.stop()"
],
"outputs": [],
"execution_count": 33
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Step 2: Recording audio data\n",
"\n",
"To make a recording with the microphone array, you must specify the **length of the recording** as the number of **audio frames** to capture. Each audio frame consists of samples from all 5 microphones. Given that we are using 16-bit audio (2 bytes per sample), each frame will contain **10 bytes** (5 microphones × 2 bytes per sample).\n",
"\n",
"Thus, recording **N frames** will produce **10N bytes** of data. Note: The simulator returns a fixed length recording at 44.1 kHz. The real car will return a recording of the length you specify.\n",
"\n",
"The following command records `N` frames from the microphone array and stores the result as a numpy array:"
]
},
{
"cell_type": "code",
"metadata": {
"ExecuteTime": {
"end_time": "2025-11-28T09:54:07.857751Z",
"start_time": "2025-11-28T09:53:57.418636Z"
}
},
"source": [
"Fs = 48000\n",
"duration = 30\n",
"N = int(Fs * duration)\n",
"\n",
"sd.default.device = 15 # or a substring of its name\n",
"sd.default.samplerate = Fs\n",
"\n",
"samples = sd.rec(N, samplerate=Fs, channels=5)\n",
"sd.wait()\n",
"print(samples.shape)"
],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(480000, 5)\n"
]
}
],
"execution_count": 53
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"At this point, the microphone data is **interleaved**. This means that the first value (`data[0]`) corresponds to the first sample of microphone 0, the second value (`data[1]`) corresponds to the first sample of microphone 1, and so on. For example, `data[5]` contains the second sample of microphone 0, and the pattern continues. To visualize the interleaving of the data, refer to the table below:\n",
"\n",
"| data[0] | data[1] | data[2] | data[3] | data[4] | data[5] | data[6] | data[7] | ... |\n",
"|---------|---------|---------|---------|---------|---------|---------|---------|-----|\n",
"| mic 0 | mic 1 | mic 2 | mic 3 | mic 4 | mic 0 | mic 1 | mic 2 | ... |\n",
"| frame 0 | frame 0 | frame 0 | frame 0 | frame 0 | frame 1 | frame 1 | frame 1 | ... |\n",
"\n",
"#### Deinterleaving the data\n",
"\n",
"To work with the data from each microphone independently, the **interleaved data** must be split into separate streams for each microphone. This process is called **deinterleaving**.\n",
"\n",
"Write a function to deinterleave the audio data and store the samples from each microphone in a separate numpy array:"
]
},
{
"cell_type": "code",
"metadata": {
"ExecuteTime": {
"end_time": "2025-11-28T08:48:59.901746Z",
"start_time": "2025-11-28T08:48:59.896773Z"
}
},
"source": [
"### Student Version ###\n",
"# TODO: Reshape the data into a matrix with 5 columns (one for each microphone)"
],
"outputs": [],
"execution_count": 11
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Plotting the audio data\n",
"\n",
"Once you've extracted the audio data for each microphone, you can plot it using Python. **Matplotlib** is a commonly used module for creating plots. Plot the audio data from each microphone to visualize the sound captured by the microphone array:"
]
},
{
"cell_type": "code",
"metadata": {
"ExecuteTime": {
"end_time": "2025-11-28T09:54:39.849569Z",
"start_time": "2025-11-28T09:54:39.067620Z"
}
},
"source": [
"### Student Version ###\n",
"samples_reshaped = np.array(samples)\n",
"fig, ax = plt.subplots(5,1,figsize=(20,30))\n",
"\n",
"t = np.arange(0,N/Fs,1/Fs)\n",
"ax[0].plot(t,samples_reshaped[:,0])\n",
"ax[1].plot(t,samples_reshaped[:,1])\n",
"ax[2].plot(t,samples_reshaped[:,2])\n",
"ax[3].plot(t,samples_reshaped[:,3])\n",
"ax[4].plot(t,samples_reshaped[:,4])\n",
"for ax_in in ax:\n",
" ax_in.set_xlabel('Time (s)')\n",
" ax_in.set_ylabel('Amplitude')\n",
"fig.show()\n",
"\n",
"from scipy.io import wavfile\n",
"wavfile.write(\"./audio_beacon_67676767_10s_driving.wav\", Fs, samples_reshaped.astype(np.float32))\n",
"# TODO: Plot the data for each microphone"
],
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/tmp/ipykernel_5220/1805528391.py:14: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n",
" fig.show()\n"
]
},
{
"data": {
"text/plain": [
"<Figure size 2000x3000 with 5 Axes>"
],
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2t2fJVOLtx3XHNO737RrSMtL82LELiWpbs0Kh93ul2UsF6Ue8dVpWgKp+eKA61al4RfvL7Ux8svq9m5U1dmTK4GLd9pUqzGe7pK0/ckH3fr1elyw+x3uiL+nz5YWbH6ooer1l3Zm78cUoVQzwdfp+C2PMj5utfi+JUpL/7ojWoOZVHD5+LiFFof4+8vC4ssy/0uqad5bqxwc7qZKbfVZcqc7zszXx+qa6s3OtQj/XaMwK5LaoFqJqFcoV6Tu47USs9p9J0P58Ok0/WnRAcvK8fbk9+N1GtzsnOIvRaFRGplFedq53LqdmqJyPp0vaBUjS4j1ntHTfWT0/qLEup2bI39ezWK7N7Xl0xmatOZSVqb5z4gCn7ANwxGg0ynCF1RkAZ0hKTdeMdcd1TZNwVQ/1d3VzcBVKSEnXthOx6li7ojw9DEq3uCfwKqF72z82n9D4v3ZqxbN9FFzOu0T2eTVyyhXexYsXrX7OnDmjRYsW6ffff1dsrHvN+QEUlx/XHXPYgfLqrF3adiJOY3/eqrSMTM3dGaM90Ze05XhOhkdxj/qdn6vk1uAPluv4hbxLrizcHaP2kxeYf283aYFe+nOHw/Vnb4/WoPeXF0Nrs/R/b5ke/C7/id/TMoy6ddoanUtI0dlLKUrPyLS73u7T8VZZJKN+2JTvtjMzszrOTOXzZm49ZX7Mo4g3Ds6+4bDMjnLGCMmDZxPtLo+7nOYww2HRnhjd/836Eknjveerdbr36/V223L8QpLu/2a91h46f8X7WXvofKGykm7/fK1VEMekuMsS5lbruX9sli0tBfNZXExKy/N4UxzslU40WXf4gtpNWqAHv9vg1Da4s/1nEvShnazEoihL2U/j/9pZoNeTkJKuH9YeNR/3/t0RrZf+2qnrP16ptpMWaMGuGO2NvlTIrLyCnT/cIZBw5lKytp0oe9f5RqNRo6ZvUvvJC3QhMVWrD543zxO48ehFNR4/R1P+3ePqZuIqds/X6/X1qiN6d8E+tZw4zzz45ko89ctWXfvhcqVZXGOnpmdqzaELUnbGIlCSPl9+SB1eW6iDZ8kGK83OXkrRmB83a00x3Ju5kyn/7tHEWbs0+IPi6xtB2eSse6Rbp63RrdPW6ptVWVVa0jNzzt/nE0umtNoTP23VpeR09XxzcYns72rllEDOH3/8YfUza9YsHTp0SMOHD1enTp2csUuUUgkp6eab4eKw61S8bvpklbnTdtuJWK0+WDIXCeN+367Plh1SanrWAdMyqGOZCTL+r5yOSstOxWFTrcuIGY3GQnfKX0pO07BPVunRHzfrgW+tOyN3norXi/l0kr5upyPiuzVH83zO/jMJdjJdii9w8cgPG3XknG0gYdXB82o3aYHaT16gtpMW6K8tJ62CYdFxyRr4/nJ1tZjAuyCd2VtPxGrIR47LxtmTmZn1t3J0Unb2+AfLOX5WHTxXLNvcdiJWz/22TWcvpcjeAI41h86r5YR5ev4P+5+pe7/eoAW7z2jirF0O9/HXlpN6fMbmKz4GLN57Vov2nLEbNHp0xmYt2H1Gwz9bY/OY0WjUmkPnFZuUal62P+aSBn+w3Dz30KoD53T9xys1Y90xDf9sjdXnKS7JNkU5KTVdSanpmrMjWinp9gOM9j4Pzu70Lo4Ol4l/79IdX6x16oj/79Yc1WUndg7l1fIvVmSVtlqw+4zT9l9cvll1RAPeW+aUQOk3q/M+5udn1PRNajlhnlpNnK/Plh0stna52oS/HR/LTF76c4de+GOH7vgiK0Nyf4z1Ofz+bzeo/3vLdM9XtmXSvliRf6ZeXt+N+mEBNsvOJ6TkOfdOcdgTnTP3VIfJCzXko5VuU84xKTVdhwrZ4Xc5NcPqnJSZadS1H67Q7O3RupiUpmveWaoR09Zo9PTNupiYqjeyr5umLnX+Z90dgqNJqen6cOF+7bczh2FJGj19k+7LHsBx9lKKFu2J0R1frNXXK62/R4kp6Rrz42bN2XHa4bauVFJqeomUTC0IU+lny5LB9q5V8mI0GvXlisP6deMJ7TgZr1UW91FvziVgCdeZ9M9unb2UYnUvjdLnpT936O+tp3SLnXuz0iomPlnfZl8/O6ooMmPdMa08cE47T8Wp/7vLNGcH5WmvRkmp6erz9lI944SS4tuy52P+deMJKXvws8nXq67s/i4vRqPtXJ6RweWctj84eY4cqx15eGjs2LF69913S2qXcHPxyWlq9vJcNXppTqGfO33tMbs3zcM/Xa0NRy+aO22HfLRSI6atyTcTpaCMRqPG/e54kvI35uxR1DtLdfR8ouo+P1u1nvvHphPlx3XHzf+37FzdfCynM/5yaoY+WnRA/1jMU5OclqEtx2PV6bWF+mGt/QPx7V+s08ajF62ySCwt3XdWl/KYX6ao2RzNXp6rWs/9o1rP/aNxv2/X+YTi61ycvT3apkxUbnGX0/TYjC26aeoqGY1GpaZnav6uol0cnY5z3On1wcL9dkcbv/DnDkW9s1SfLbOe4+DIuUTd/dU6bThywbzMstOroAozwrl7/cqF3v7J2Msa+vFK/W3xuRny0UrNWH9c7ScvsPocmrw7f5+Ua74oe2LiHb+fj83Yoj+3nNIPa/PeRkG9PW+fTllkzFxMTLX6Xn2+/JBG/bDJnME1a9tp3fLZGqu5nx76bqN2norX/d9uUGJKum79fK22Ho/Vc9kZWpK0YFeM3l+wXy0nzrOaHysj06gm4+eqyfi5evh7x5ll9hK0ao+bLWX/LXq/tUSL9uRk1BVHcK6o6dTHLyTpoe82aM6O0/py5WEt338u3xF0FxNTNXPrKZsAXZqDzLnc2k2ar/UW35ni9MHC/Zqz47QGvLdMLSfM068bT+iVmTuVmJJuFZCu9dw/GvT+8mI9lhWnl2fu1J7oS3pvwT6nbH/+rhgdPpeofQ46a1PTMzVz6ymds/P+/LPttOIupynucppem53V6ffrxhP6ePGBQrXhcmqGEgtQDrOkfJ09ui0+OU3Xf7xS05bZzmnzb3ZnsSnr7pSDDL5VDgaYmG6CklLTNfL7jfpry0mrjNMTF5MUnescZTQalZ6RaZUxmpaRqYxMo9pOWqAOkxcqOS3D7t+qOAx4b7n2Rl+y+r5/ufKwPl58IM/v/JlLybrxfyv1W/bNZl4yM41Fylru9+4y9Xl7qTYduyij0ahFe2J0Oi7nbzJvZ7Qe/m6j+fP64p/b1Xj8HLV9db75b3HmUop2nso5b59PzAr8L9gdo9avztcFi4EAlqLjkjXu9+2FPuc7Ctbc/dU61R43W0v2Zg1aWHXgnB6fsVkXEu3v3+SDhfvV560lVgMWrsTb8/bp7fn7dI1FxofRaNRfW07aDZrFJaXp900nbL7LmZlGfbfmqObtjNaBM4ULCiWnZWjWttNauOeMTly8rPaTF+jerzdo+f5zeiVXwPXlmTv199ZTevj7/DOyiyI5LUNNxs9Vp+xSxvlZc+i8Bry3TP9sK77A0vsLcrIoLT8+sUmpGvn9RrWcOE/vzN+n6LhkXffhCv284bj9DWVbceCc1SAcy89k7uu1Pzaf0M5Tcbr987Xma9XLqRl53mugYFLTM3XHF2uLLUu2LFl5oGxlchSE0Wh02nm8pB0tpn6ZwsrMNNpc13+4cL8+WpTzHZuzI7pI9132BsuuOnhO4//aocupGVkDJH/frts+X6vBH6zQ3phLed4rFtWCXTH6fVP+11VXas2h8+rz1hKtOlA8A0ivJgt3n9Hhc4n6eYPz/k6Z2edty3uIv7ee0s5TceZB58Vha3bfZO1xs23m8qxWwX4gpyTmHr0aOGWOHEcOHjyo9HT3uSmHa31nMer3+o9X6pt72ivE3yff5xmNRj3/R1an6uDmVaxqkF6yuFG0HDX+5crDevm6pna3F5eUplumrdF1LavokV718tz3igPnrAIx9hy7kKSeb+YEHp7/fXuB5mZR9sjB9AyjbvhkpQ7lKmdlGfB64Y8duq1jTfPvh88lasa6YwWaY+LPzSf1n/bV5evlqdT0TK09fF7taoYWW1mWH9cdy7dz31k2HYvVvV+vV4ZRVvP6zNsZrUC/K6/RefBsooZ8tNJmTgDT63393z0a2KyKqoeWk9Eojf5xk3actO7EGfBe4dOtTfPfFMS6wxd0Q+uq8vMu+N9z3O/bteV4rMb8uFnXtYy06XD51uK7uurgOXWpW0lrD+d0tNd67h91rlNRPz5om3FZkBHExdVZ/tOG41p7+LwWP9VLBoNBrV+db/X4pH92S5IW7TmjNeP6mkdCWZZLO2QxirXpy3Pt7ud+i2y35//YroHNIxQTl2LuxM1PXiEVU8bPvV9v0JEpg3X8QlKh/v6OLNpzRv9pV12S5Jkd1ElISVfc5TRVCfLTgbMJqlc5wDw3zL6YSxr2v1XmY+rcnTmBpcSUdJ25lKywQD+7+7rjy7XacTJed3eppVeG5Bx3H5ux2e76uSWmZujmqavVo0FlfXtvB/PyT5YcVMUAH/PrKCrLDr2nfskaDbXu8AWbbJ1dp+PVdtIC7ZzQX+V9i365ZKrnfuBMgmqE+svHq/jG0BQ0OFZYlhmdW1/uZ1Pj+JEfNmrB7jOqHlpOy5/pk+/2TO9zZIifQsr5aNOxi/pw0QFtGX+NQvx9FHc5TUF+XuYylJmZRjV7Za4yMo3aO2mAfL1cXzZM2QGu7SfjtPV4rLYej9Xm4xf1ypCmdr8LZy+l6Kd8OkxzW33wvDrXragm47OOPf/uiNaPD+QcV02d5wvG9lS97AwcUxC4ZbVg83r1X/hXK5/L+bs8NmOz5u6M0cTrm+q6FpGqUD7/a63CWHPovPq/l9Ox//umrAC3n7en7ulSS+Nn7lDzqsEa3r6GeZ1Js3Zr07FYbToWq2FtqzncdlxSmnq9tVg1K5bXH490KVSp0hMXs47tc3ZEKy4pzTz/n+kcbirlGhrgo8xMo2asz/p7JaZm6MTFy3pl5k4t3JN3hp7lAJi/tpw0z9n22IzNWnv4gn5cd0y3tK+ul65tokEfLNe4gY01oFmEJOnLFYd17EKSKgf66pFedZWRadSQj1aqYoCP2tSooB0n4/T84MaqWzlAS/ZmXdPcnSuby8vTQ2/d3NJh+97JHnTRbtICHXit8HML5rb52EWbZXN2ROuxGVskO/Po3ffNem04elE3tj6nd4a30u+bTuiTJQdt5nwyHQvyc+ZSsgIsjsf2MmFmbj1lnt/wVweBwtT0TM3eflqn45L1UI86RZ4XzXStfjEpLd+5O3acjDOPQB81fZMaVempGeuOaWSvegotwHfS0fbftQjoW05m3GpizjXQBwv368TFJG0/Gadnft2mtIxMq/sIy33c8cU6q2UGg0GZmUZ5eBjMnUMmT/y0VZ4eBmVkGrVlWqx2TOivjq8tUHxyunZN7C9/n7zPnbtOxWvB7hg91LOO2xznczt6PlGjpm/SyJ71NLiF4zn2itusbae0fP85Ld9/TmP61i/Uc41Go7afjFP9sEC3KLtZGFuPx2bNMdm1ttX30vLYExFk//qzJMQnpykz01ig41VxMp3r37+llUvmBl2276yW7z+rZwY0Ktb5tz5ffkgDmkWoWoXCzylz+Fyijp5PVK+GYZrw905tPR6rGQ92Nl9n74+5pGMXkhQdn6xbO9TQozM2a9a205rxYCdlGo36Z9tpc3C6U52KCg/yMwdXjkwZrFOxl1UpwNe8vYSUdKvzj6UzuQYupqRnmO/fAny91KxqsN3nFTfTPWr7WqFOnafHdC679fO1V81cicWlMJUtpi49KC8Pgwa3qKIq+WS4WAZITEHf9Fz7GvzBCnWoFaqfH+6slPQMbTkWqzY1KxT5Oz36x02Kzv7sL9571uq12XuVl1Mz1P+9ZWpbs4LeHd6qSPtEFqcEcsaOHWv1u9Fo1OnTp/XPP//orrvucsYuUQr5WnRmbT0eq/8tOahhbarpvm/Wq4K/j/7TvrqC/LxsLlYsD0hJeZQZed5iBP1XK4+oa91KimoSLqPRqEdnbFFkiJ/GDWysT5Ye1O7T8dp9Ol7hgX568petmnxDM7s3OHnNr+BIYcr0tJwwz+aAm5+0jEz1zidjxdJLf+3US3/tlCTd3aWWvl51RP2ahOuzO9sVar/uavFe2/JpBZl3p7j0eHOxohqHuaw80x+bT+pScpo+ub2tEpLTC9Rhd+x8TvBiX8ylPGurv/DHDrsn3tUOsjTWH7mo9IxMeXl66KHvNmjuzhgteaqXalUqX+DXVBhHzieZb3YcuZyWoZYT51ktu5CYqnu+WufwOY4kpWao4YuFyyr8c4v9jLlNdjrICpvF4Mj8XTGq+3zW+/JEVAPd3qmG2k7Kmg/rmibhVnNqdalb0WHGgCy+T9Pv76gu9SrZPG4KXv615aQ5kJOQkq7Z2wuXJWcZjD14NkFvzMnK7jAFcn5aX3wB412nHY+af3XWLrWpWUHTlh1SYkq6Vj7XRwaDQbFJqUpNz1RYdofCxqMXNOyT1bq/W229eG0TSdLKA+d02+c5gbgudStqenbH/LYTsXp73j493LOuOtetWKB2JqSk6+apq82/25u3q7hL3528eNkqkJOSnmE+vh2/kNVRfvR8oj5ddkgPdq9j83zLYNMTP1mXEfjPp6s1/tqmuv2LtbqtYw1NvqG5flp/TBXL+5pfR3RcsmpWdM7xorBylyydvT1aCSkZ5oCjZR+n5XxzBXXr52u17oW+Vstyd5xK0rxd0aoXZj3wZOsJ63JmlmUgTYHY8X/t1Pi/durga4PMAd3i8PLMnXaXvzprl5bvP2sOQlgGciwzSUydxCbpGZlaefC8WtcIMR+rLybF6mJSWp4d3lP+3aNFe2K0LyZBb97Uwrzcw2DIM8tvw5EL2perDN6D323U7jyOC/Y8NmOLHpuxRYue7KldFlk8M9YfNweJHv5+o2Y/2l3frTlqNeilcZVAhfj7mI9Fy/dnjXLdfDxWi57s6XCfx3KNbD58LlH/7jituzrXsgpAF/baUtk33A9+t0F9GoXpnq61JYuBAJa251FKb8PRrPParO2n9c7wVhr7s/1SImsOndeAZo47yVPSM/Tj2mN65e9dur9bbfPyqHdsr1ce/XGzOZBj6dj5JNWomNWxdcP/VpozrWpXKm8OrhWW0aKrItMoeTr4WhmNRptBTn3fXipln39+uD8nYJuclmEzGCcpNV1Nxs9VBX9vbXzxGs3dGa1fNp7QvV1rq6Ass+ZyDwhTdkdkp9dtM4sSU9LV7Y1F6ly3kjLtjB0wHasTUtL18HcbzWWF9kZfUusaFRSfnZ0T5OetpNR0Ldl7Vj0bVFZ5Xy/dPHWVErOzL8cNalzg1+JMRqNRqw+dV5MqQQrx99HgD1YoISVdo6Zv0uAWheuwzC+4l9f6V1IS97dNJ/XUL1vVpkaIfn+kq9Vj20/EKTk9Q6diL2tw8yryyu7AM12rF8ax80mqWqFcsZ5Prs+e97OCv49VkP+G/+WUIA8L8jX/32g05ntuKC6ZmUa1eCXrnLTn1QGFGjRXUM//sV3T1x6zGrBx0eJ8OeHvXXkGco5fSFKF8j569MfNio5L1p2da+rLlYf15d3tbYIl5xJSVLG8T4E+o3d+mXWPlJ5p1MvXNdWbc/doyd6zqhpSTv2bRuQ5ICM3y71N+me3Jv2zW02qBOmjW1urTmXbMrGS9OTPW/XbphN686YW+mzZIZX39TKXFu/XJFzzsu9jft5wXLd3qqldp+I1yGKumsjgcpqVnQn56dKDNv0FN01dren3dzT/vvnYRd3wv1VqGhmkfx7trs+XH9Kkf3brxtZV9c7wVnpz7h5tOxGnL+5qLx8vD5tOa8t7w73Rl/IN5BTl+5eb5fX/2YQUpwVy/jsn7xKbe6Lj9fB3G/XENQ3Mn9XMTKNem71bLaqHqFfDygrKHmAbHZesm6au0ogONTSqd94Dqp0lMSVdp+Muq15YoMN15uyIVmSIn1pUC3FKG45fSJKvl4f5nlLZn0HT/IuT/tmtnRP6KzElXRlGo/ZEX9L/Fh/QN/d2kL+Pl4xGo/nYKUnnErKOGfau/dZlXw8///sO/bYp6zpi/HVN9NvGE1pz6Lye6t9Q4QUMlucu+XzRIvt7/q4Y9X5riX5+qLMqB2YdsxfsjtGxC0k6diGJQM4VckogZ/Nm61G3Hh4eqly5st5++23de++9ztglSqFrW0SaR8hL0rlLKeYRnScuXjbfGDaKCFLDiJwDa7pFrccNRy9YPWYp90jY+7/doInXN1WV4HLmElKfLrUujfJk9qjhF/7YoVoVy+vExSTtOBmvFwY3lp+3p01HSXErzI32jpNx+nLFYbWrFVrk/ZlKxczbFVOkkl9Xqx0n4+Tn7eHwhO/qOTYW7D6j+i/8K0la/kxvVQ701axtp9WjQSXzyPELial6/vftGtGxho6cz+kIym+C3MPnEjX0Y8dzCKVnZGprrlJw3d5YrP/d3sbcmdjrrSVqGJ7z3i3dd9Zuh3RJapMre8cVbvyf9TxZaw6dN3f+Fad3F+yzGsFrGcRRHmWfcpu2/JCqViinGqH+MhiyRuRaXsBlGrNuPtMyMs3H1sL6d/tpzd0ZbRX8OnQ2QZfTMvTsb9vzfG5xseyEVXZmZvf6lc2jnbe90k87TsTp1uyAzecrDqthRKB8vDzMo9RNTO/t4r1nzPOkLN131mY0W2p6pk7GXlaov4+C/XOCKD+tP27VuWwacWU0GvXID5uUkJJu7gAuLoM+WK5u9Srpu/s66K15e9WqegWbdW7/Yq2OX7is6XbKJH610vHcL/tiEnR79nwyP6w9phEdatj8XQ0yKDktQyOmrVGr6iF6+bqm5kw/g8Ego9GoKXP2qFOdiurdMEzHzidllW7NvmnefiJO4/7YpuZVgzWoefGPpl6276wGvr9cw9tVczgvVmHknlfHMhBo8t85ezWsTbVCBxpMziek6GxCiu75ar0uJKZq2TO9C9XZWBhLLDpLxvy4WW/d3EIT/t6lFRblOOo8P9uqU6zT6wvNN6GWxvy4SX5envrw1tZ2R/pbltx9+tecMrgZmZlakut7YVn2M3cQR1KR31tJ6pPdQe/IIDsTId/79QZ9cZftgJoLialWmRX5iXpnqTIyjfp5/XEtfqqXw/UsO4xjk1J1xxfrdH2rSN1vEYy9/9v1WnngvJbvP2cO5OSOK363+oj+t8T+/EA/Wxw3U9MzVeu5fxy25+HvN+mJqAa6qV01VQ0pp/VHLujFP3aoeqi/7u1ay3x8VfYxtigGf7Bc2yf0l7LnjTTZH3Mp30DO31tPKfZymhbujlGtiuV1/EKSPhjR2qoTIyPTaNOh/dP6Y3pz7r48SyKZykRlZhr13sL9+mDhfv02srPa1gzVluOx2nYiVuOzB2BdTErTJ0sP6s25e6XsbNuCym8wRQcH5eH+2nJSp+KS9VsByvXM2Zmzjz82n1TDiEBzx/e+SQP1zK/bNGvbafVvGq5P72hnDlZscVBR4GJiqjm7esv4a3T8wmU1r1b4ke3L9p2Vp4dBXe0MPsntj80nNfbnrYoM9tOqcX1tqioYjUbN3h6thhGB5o52ZX+PNh+LVff6leTl6aGdp+J05xfr9MQ1DXR7p5r6Ye1RbTx6UW/e1NJu4GPxnjMa+/MWvXVzS/VtHG6T0X7rtDVadfC8fn24c573fpdTM8xZsJuOWb+vR84l6rqPcsoJx19O0x2da+nRHzdr5tZTmv1odzWJDMr3PVJ25tujP25Wu5oV9MvDnfM8h5yKvSwvD4N50Jdl5mJMfLKm399JZxNSrDK3n/xlq8PggGVfgGnw1ps3tdDNBcjYNhqN2n36kuqHBxR4FLppsNd9FsfHk7GXVddB0CE/nyw5qGnLD+liUqqubxmp925pbX7MdB0V9c5SHZkyWJmZRqsKA0mpWdn0Hy3ar/rhgXrm123m4MLBswnmALGJqTT067P36OPb2kjZx5rfN2cF+25sXVV3dK6pT5Yc1Pjrmigs0E9pGZkOM9G/WnlED/esq48XZx33d56K17xdMbqxTVWHn4H45DSNmb5ZO07G6bM729otMb3rdLz6vL1U0x/oqC51bb+npuOP5bndZJ7FfcyLf+7QtS2q6OWZ1qXOdlv0c9gb9ClJ36w+Yv7/7dnnHNO5wtRn9fvmk3pneCvz65+3K1rXtrAdOGApPdPocO7AEZ+tMX8vcn+Gt52I1brDttlp20/EacuJWN3WoYZ+3XRCX608os/vaqeKFsFM03dk5YFz8jAY1LF2qFIzMvXFisO6lJyu5wY2yrPNjpyMvezwnG9iqj7y2Iwt5u/64r1nrM7dD3SvrRcGN9ELf2zXiYuX9ebcvRrZs652nopXoyqBxZr1ZY/pGshoNJorcHxxVzv1bRxutV5qeqYOnk0wZ2odem2Q7vpqnepWDjAPVszMNOqpX7fK18tTr93QLCcYn5KuLcdj1bF2qLw8PZSclqFVB89ZDUQ3Go06GXtZ3f+7WLLIas7MNJoDjyanYi9blbWVpCbj5+qLu9qpZ4PKNoNqJvy9U3d3qWX39Vt+7r5ceVjjr2tivlf/ZeMJPT+okR7sUdfucw+fS9T3a45qRIfqNt/5dpMW2Kz77oJ95uNajwaFnwIA9jklkLN48WJnbBZljFeuYWuO6qXe9vkaSQY9cU193daxplVt5xf+2KGW1UKUkp6ht+bmP1fA+L92OkyJtd1vzk3jd2uOaueE/jp50TU1Xe259sOsC/HfLebouBJFKfl1tTK99+MGNtKIjjXyXd+VvltzVOcSUsylbiTpveGt9PhPWR3MljfdV+rXjSfMN4+WouOTbYIUey3m3th5Kt6qUwVZ3H0S0MV7z2rxm0s0oGmEhneobjOBe9zlNJvydoU18gfbeQ36vL20yDcgxWH09M1WAwgOn0206mSUgxtNk+9WHzFnRZqYOjhnju6qs5dSdN83OZkfD/aoo6f7N5SXh0FJuTqUTEHj/WcS9K8TJ01dceBcnplupswce0zz5BSE6dhqaem+M+b3a/OxWN3Wsaai3snqqNg7aYDG/rRV/2w/rU+XHlL9sABzyaY14/rKz9vD3Gm142R8vqVRi2r36XibuTmK6uylgpWa7FjAOTnsSUnP1B1frDNnxXSZskiNHAyKKU5/bz2lID8vuwG/L1Yc1j1da2n1wfN2gziy6PAe+P5yPdC9joa1qSYPD+mvLafUvb7jDtppy607/n/beKLIwWVnsvzeF5RlZ+/s7afNI3KPnE+yCqJJ0i2frdaMBztr7aHzGv7ZGjUMD1T72hWUkJyu7SfjtP1knDmQs/V4rM08FGkZmeYMG2UHQHIfyyTprbl7tfn4xULPY2EaZLDquT7mzMO9MZe0YHdMvs/N7ej5RNXINRr5Ukq6UtMzlZRqfRx9e/4+3dW1loL8vBWXlKazCcmqWznA3EGxN/qSxvxoOUgx633NXX712IVEqwE+x84nFXjAQXpGpq79cIV5fq1hn6zWO/9paTeDyRTEuVJfrjisibN2aXCLKvr41jYO17MsrVoY364+alWa91xCirljau7OGKvAXqpF5qYpQ++tuXv1kUVWcq+3lig2KU2/PNxZ7e0EMkxBNVMpsQNnEhQTn6xfN57QH9n3SvsnDzR3ENobAf/XlpPm9/yUgzkzl+w7q1HTs65NLAdh9Hl7qS4kpqpyoK+WP9Nbz/62TecTU/Xinzvk4+mhF/7I6lj+fdNJu0GHe7LnF7jvmw1qFBFoNbjPaDSaB4LcNHW11X4tM7gyMo1qPN5xlnjua+2X/tqpm9pWN8+tOuiD5fro1tbqXKeiKgb42t1GbFKqDpxJMM8rsuHoRY2evtkcJMgtMSVdXSwyRJXdOfrN6iPmDO5Fe85YlS02mbMjWp3rVLTJojdlqloG5J/+dZsOnE3Q/J0xurVjDW05Hqszl1L0/X0dtWzfWb02e7cm39Bcu07H69XsOaBGdKih129sLmVnEdz95Xq9MqSpDp5N0Jn4ZK09fEFv3dxSD367QafikvWoxXHAUfZzbFKqpvy7Rze3q6a2NUO14cgFvTVvr169vpnqZw9ie8Mio+HPLaf03i2tlZiSbp6o3NKhc9YDDZLTMtXv3aWKic+5Vvh980k9N7CRRuRx3/DP9tNqtSwreGTZEf/75pPmvoTFe8+YJ0df/0KUeRR9bvaCErXHzVavhpX19T05JZHPXErWs79u04XEVPNg2GGfrLZ5rqVbp2WV6tp5Kk6Ldp/RAz3q6KdCDmr7ZcMJrT9iXeUgIyP/wbJWZaTzyIi744uca/535u1T/bBAm0EOlpbuO6ul++wHjywrWjz96zYF+nmbBxYM+Shr4OTUpQf1/f0d5e/tpRoV/c3XtS9ZzMvz4h/b9cGInIDg+YQULd9/1qZUpvk1dK6psEBf9Xpzifo3jdBL1zZWSnqmnvl1m2ZuPWUeTKDsQHtMfLL6N42wmnPFJC4pTcH+3jIajYq/bH86gdzz+U1bfljlfLysytd+uOiA3l2wT3Url1elAF89O7CR2tSwHTxWUAkp6TIajTbl9S8kppoHcM4a082iTYesAjn/bj9tcx+6+XisueRl25oVtGzfWf1iUcK1buXyGtIqUofPJprn7L6pbTW9eVMLjfx+o00QMT3TaJVtaDr/jfxho825N9rBvMP3fbNB/ZqE2yz/auUR9WoYZvc5uSuprM31+2uz9+jBHnV1MTFVJ2Mv69vVRzRz6yl9e29H3ff1el1KSdcXBRxUY3nNv8zB9wCFZzAWZAKDQurTp49+//13hYRYp57Fx8dr6NChWrRokcPnlkXx8fEKDg5WXFycgoIKNsrlanA+IcVc2qeg6oUFWNUjL0mNqwQpJT3DZu4aAEDJqlnRX0fPu0dgfcKQpg5LSxWn2pXK6/A52/PP/skD9dbcvfp02SG7z3O2BWN72C1vVBJC/L0Vm2R/Uu1v7u2g+79Zb+6UQI4eDSpzM1WGOPp7No0Msum4PTJlcJ6ZMaue66PIkHJq8cpcc4ksSapY3kfnc3XEdK1XsUxNOv50/4ZWQZKdE/rr8Z+2KNDXq0CDpnw8PbRv8kCdir2sIR+tcBiQtOeXhztblc2UpMqBvgUO7F6pJ69poLfn5z8g7krc3612ntlUU25srl82ntC+6Et67cbmuYJnOYa0jNSNbarqiZ+2aPFTvfTinzusRi6/fF0TxV9Ot8o8NlnxbG91eyNrwGl5H0890rueqgT7qWX1EC3Ze9bcwW+S+/tyZMpgvTNvrz5YlBVg2vpyPz3/x3ZV8PfW92sKV+7VFIyZ/M8um2BzbvsmDVSDF/81/7706V6asyNaxy4k6Ye1x9S3UZja1qqgDxce0OU06w7oznUqKqpJuG5uV00r95+z6Zi8p2stfbXyiHJ7qEcd7TwVrwnXN9XPG45ryZ6z+vmhzur3nnUQweTHBzqpU51QmxHaf2w+YVNWtThMGtrM7gTzuf33phZ6Jo/BNS2rBeu+7nWsgjQFMf+JHqofHqjktAytO3xBHWqHatGeM3rE4v09/Pogm0Ewr93Q3DzXr0lksJ/dwOFvIztr0Z4z5uyPkuTv46mRPetqeIfqCgv0s/oe/KddNYcTte+Y0F8Bvl46cOZSka8NLb93T0Q1sPtdLqzWNUK0+Vj+cwnb06lOqNYcclyetbi9OrSZ/tp80mrghMna5/s6HMiz6aVrzAGK/Nrs4+WhB7rXNn+2GkUEmgcSmCwY21NP/bLVnDG58Mme8vXyMB9DLb18XRNN+HuXKvh766LFdfkP93dUpzoVNdUik7Qwnh/USOFBfhrSMlIv/LlD09ceU7d6lfRAjzpqXSPEXKLNJDPTqAyjUR4Gg7mk+L5JA63mJ73v6/XmANLM0V3NAbMmVYL088OdFeDrpSn/7rHK8jZ5Y1jzIlWEeKB7bbvH+V0T+5vnxTS1Ne5yWpHKM9vj6N6xIPK7XrxSzK1kqzBxA6cEcjw8PBQdHa2wMOsI4JkzZ1S1alWlpdm/6S4uH3/8sd58801FR0erZcuW+vDDD9WhQweH6//yyy966aWXdOTIEdWvX19vvPGGBg3KmRDUaDTq5Zdf1rRp0xQbG6uuXbvqk08+Uf36BZt4kECOfZbRcAAAAADO88yAhvrvnLw7U/LqKLLUoVaoudY6sji74wMlK/ff8+2bWxZrNt+o3nUL1ElfXN+1Po3CbMrx1alUXoeK2NGX271da+v2TjUUGVJOHyzcr1UHzzssm1fafXZHW83ZGW1V8SC3QF8vXUqxn6FQmtgLSJWU7vUrFXvJ4LKqSrCfTjvIJCwubWqE2JRsdKXHo+rr4Z515WEwaF/MJbuZ/avH9ZGHwaDNxy7q/YUHrErndqwdqrWHrY+tv43somGfrLLZTklY93xfh2VOS9rzgxoVqqpCYR2YPPCK54Uqa1wWyNm2LWu0Q6tWrbRo0SKFhuakPWdkZGjOnDn69NNPdeSI7aiP4vLTTz/pzjvv1NSpU9WxY0e99957+uWXX7R3716bwJIkrVq1Sj169NDrr7+ua6+9VtOnT9cbb7yhTZs2qVmzZpKkN954Q6+//rq++eYb1a5dWy+99JK2b9+uXbt2yc8v/4mgCOTYZ1n3GAAAAIBrNa4SdEVzBF3Ndk8ckGdpK5QuW8ZfU6g5qgAA7mVoq0iruVbdWXA5b8Vddm7Sg7uwnBsTWVwWyPHw8DCn09rbbLly5fThhx/q3nvvLa5d2ujYsaPat2+vjz76SJKUmZmp6tWra8yYMXruueds1h8+fLgSExM1a9Ys87JOnTqpVatWmjp1qoxGoyIjI/Xkk0/qqaeekiTFxcUpPDxcX3/9tW655ZZ820Qgx77YpMJN4AoAAAAAAAAAKH2WP9Nb1XPNZXi1K0zcoFhzmQ4fPqyDBw/KaDRq3bp1Onz4sPnn5MmTio+Pd2oQJzU1VRs3blRUVJR5mYeHh6KiorR6tf2J1VavXm21viT179/fvP7hw4cVHR1ttU5wcLA6duzocJsoGIMMBVgLAAAAAAAAAFCadf+v7VxLKDiv4txYzZo1pewsGFc4d+6cMjIyFB4ebrU8PDxce/bYr+8XHR1td/3o6Gjz46ZljtbJLSUlRSkpOZMAxsdTnsAu4jgAAAAAAAAAAOSp2AI5M2fO1MCBA+Xt7a2ZM2fmue6QIUOKa7du6fXXX9eECRNc3Qy3l5FZbFX9AAAAAAAAAAAok4otkDN06FBFR0crLCxMQ4cOdbiewWBQRkZGce3WSqVKleTp6amYmBir5TExMYqIiLD7nIiIiDzXN/0bExOjKlWqWK3TqlUru9scN26cxo4da/49Pj5e1atXv4JXVjZ5e5KSAwAAAAAAAABAXoptjpzMzEyFhYWZ/+/ox1lBHEny8fFR27ZttXDhQqt2LVy4UJ07d7b7nM6dO1utL0nz5883r1+7dm1FRERYrRMfH6+1a9c63Kavr6+CgoKsfmAr0M/b1U0AAAAAAEBTb2/j6iYALteqeoirmwCgjMukQlORFVsgx12MHTtW06ZN0zfffKPdu3dr5MiRSkxM1D333CNJuvPOOzVu3Djz+o899pjmzJmjt99+W3v27NErr7yiDRs2aPTo0VJ2BtHjjz+uSZMmaebMmdq+fbvuvPNORUZG5pl5hILp3bCyq5sAAAAAlEntalawWXZT22ouaQvy99eorq5uwlVr2p3tNKBZlQKseWX2vDrA6fsoTgOa2q9scjWrWN7H1U1wqj85DsHN3NLeusJR00j7g+U3vhilSgFl+/tZVhgo0FRkxRbI+eCDDwr840zDhw/XW2+9pfHjx6tVq1basmWL5syZo/DwcEnSsWPHdPr0afP6Xbp00fTp0/XZZ5+pZcuW+vXXX/Xnn3+qWbNm5nWeeeYZjRkzRg8++KDat2+vhIQEzZkzR35+fk59LVeDSTc0d9m+m1cNliS9f4v9EnlAWff9fR1d3QQARfTvY921/oUol+3/8OuDXLZvR/LqHNs9sXR1nDnDPV1r2Szz9Sp9Y7p+fbizPr6VUfMF1aVeJZtlb97UokDPffX6pjbL7upcs1jalZ/FT/Uqkf3k5mHRsXBHp5J5rZZaVg/Ruhf6lvh+S6ObCxiQXPdCX1UO9M1znaf7N9Q1TcLtPtazQWUdmTJYj/WtX6R25ubn7an5T/Qolm1diUOvDcr3fdn00jUKD3K8zpbx18jP2/Y8cui1QerZIGfA5jMDGmrCENvjyZXaMaG/nu7f0GrZByNa67eRXayW/Tmqq/55tJv+HNVVPz3YSXd3sT0fFtTK5/po40vXaObonGBHzYr+kqSoxuEK9M2ZveCJqAZuFwgL8st7doXiOu61rVmhwOcad2Jv8MPK5/rYLNv4YvFdgzcID1DVkHKFfl7XehUV4m9d5WZw8yqqHOirz+9sV6htfXpHW5tlr+XRV7f15X5aM65vnt+lqMb2j6n5qR8WoCNTBmvr+H46MmWwjkwZrCnDWmjL+GsU5OelyTc00z+Pdrd6ToPwAG17pZ8qBvhqw4vX6MiUwXrU4pi9YGwPHXxtkG5oXdVmf6FXEJhtUS1YC5/sWeTnmxS2X+b+brWveJ8mQ1tFWv1+a8caxbbtvBiI5BRZsd29vfvuuwX6ee+994prlw6NHj1aR48eVUpKitauXauOHXO+FEuWLNHXX39ttf7NN9+svXv3KiUlRTt27NCgQdadEwaDQRMnTlR0dLSSk5O1YMECNWjQwOmv42pQNaSc+jct3AH+8aj6mjWmmzrXqXhF+/51ZGcdmTJY17eyPZjLwc3rdS0j7a4LlEbd6tt27pSUID+vYrnoKU5PXtNAfRuFuboZpUqbGiFa9nRv7Z44QEemDLa5mWhVPUQ+nqWvo7go/hrV1e7Nn7I75iy9dXNLbR3fz2qZo9GdLauHqHuu7+qRKYPVuEqQKgf6at3zfVW3cnm9dXPLK34NhWEwGKzalbuNheXpYbB7E2nP/26z34nv5+1pd/nbN7dUOR9PrRnn3p2jD/Wo49TtN6liO3qxe33XZkbn1UFozy8Pd1a7WqEa3MLxqPncnbGLn+qlGqH+RW5jcSvOm++CeKRXXZtlBb15vr1TTT1o8bn86cFOmnB9M5v1gvy8tKQYAy8zHuyk2pXKu6QTcM+rA/Vwz7qa8WAn3WEnaFXex/5xRpLa18o6B8x5vLuqVbDulMsrAFY50Ff1wgI0LbvjrWJ56++Fabu5/femFnpxcON8XpF9jo6j9cICirS9onpuYCPz/xtFBJr/X6uiv/55tJs+HNFaHWuHqlu9ShrYLKdDfPfEAXp6QEMF+HqpV8PKOvjaIB2ZMlhVQ8opyM9LOyb0168Pd9aCsT0UFuin9S9EmTsEW1QLNm/nvm61tW/SQI3qXc+8rHqo9d/um3s7SJLu6lJLVYKtB3LWqli0Y0ukRadtYQYVvnJdE/P/Bze3fxycfn9Hm8wue9e3Hh4GLXiip+qHBeixvvW1/oUoLX+mt9U6oeV99FiU/X6Px/rWV4i/j3ZPHKAJQ5rq8zvb6fGo+to5ob88PAxauu+sed1HetXTXVcQPLHnt5FdFODrZTVI4f1bWmlIy0i1rVnB3Dn5aJ96alU9RE0jg9Wqeog61qmoV4Y01f7JA/WyxfspSZOGNtO9XWtr1phuqlOpvCTppWut1zF1uDeLzPkc/Tayi2Y82Ekf3dpam8Zfo5bVgvVwz7p6LKq+pt7RVlvH99PrN+Z0iq8Z11d/jupa4EExdSqXz/Nxy890fqY/0Mlu0KBtzQra9NI1enWo7TFeFh39w9pUU4Nw+8eJjrVDJUkLxvbUbyO76OZ21XVg8kDz4/llgx6ZMtj8/79Hd9Nt2R3Kjtpk7/mW2yiKppFBGt4uJ/tj+v0dFRlsO4C7YoD965eiBO7+d1sbrXi2t811mr3ztyT9/FBWP9YP93fS5peu0dzHcwLD7wxvqfUvRCnKQWDapFu9SlZZLT0bVFbd7M9Zvybhmvt4D93asYb5XDG4RRXzsXHDi1EKLuetiGA/vZJHgPbzu9rpu/s6mH+/r1ttDW5eRde1jNR/8zi/v/OfrGNicK77yhB/H217pb9u65h1Pp1+f0dd1zJSf4/uprmP91BQrqkbxl7TQO8Nb6U3hjVXvbBAeXoY9O7wVppyo3WAavkzvVW7Us53bM7j3a2CGV4ejq+Z/hrVVXUrB2hEB9vgR+57o18f7mz3fu2V65pY9ctUcvDZMjkyZbBezHVcyq0wwep3h7eyOiZMHtrMKlBdEFNvL9g9nMloi3MuCi/vcHwhHD58uLg2hatMgK/tXDn+Pp5qVytUy7IvAA9MHqg35+1Vl7qVzKN7fnywk2o990+R9tm8arB8vRzfiElSNzudGx+OaK2/t54q0j7dxZEpg4v8viF/n93RVg9+t9HVzdAnt7XRyB822Syf/Wh3VQn2U0D2aKxVz/VRlymLrmhfT/dvqDfn7rVa1rdRmBbuOSNJalwlSK9e31SVAnzV660lkqQnrmmgupVLtrMgP2OyR+00e3muElLSC/Vce+9BUfh4eig1I/OKt1MUB18bpMtpGWr28tx8132sb301rxqs3o3C5GlxceuZq3Pwz1Fd9c2qI3p55s4rbt/fo7vpuo9WqEmVIHl7eeiB7rU1evrmK95uQVzfKlJ/bbF/7G9SJUhB5bzUolqwhrevrg1HL5ofu6F1Vb12Q3OV8/HUuYQU3TZtra5rWcXujezkG5rp4e+tv7NRjcM09fa2ik9OV5tX59vdf1iQnxY+mdWBGh7kq0vJ6dp6PFafLjt0ha86f/d0raXl+89Jkr67r6NGfLZGqw+dt1onuJy34i6nqV+TcIUF+er7NcfsbmvTS9couJy3/niki579bZv2xSQ43O+g5lV08LVBunnqKm06Fmv12JEpgzXqh036Z3tW9vXh1weZO60j7NyIu5OmVR13xJiO6YF+Xlr+TG+1mmj/85CX8r5e+mtUV13/8UpJ0qwx3VQ1pJxaO/hsFadFT/ZUn7eX2iyfMqyF7vlqvZpVDdKOk/E2j4/sVVefLDlo/r21RVB06/h+ir2cqts+X6sTFy9Lkr69t4MaVQnU/F0x5vVqVyqveU/00Om4ZF3/0QrFJ9se3+uFBejAGcefufw0rhKkWWO6qe7zs/Ncb+GTPVW3coA+X3Hl902P9KqrhhGBemzGFik7gBXq76Ob2lXTzVNXS5Ie7llXft6eur1TDYffvbwYDAY9P6ixhrSM1PELSepoZxDVrDHd1LhKkDw9DHr/llbm9thTwd9bF5PSpOyR/At2n9GAZhE6fiFJM9Yd00M961p1cN/crro+XXbI5m9T3sdTiamFn3e1e/1K5mNWoK+XLmWf64e0jNTMrafUslqwfLw8rIILf43qqrAgX51PSNX2k3G6pX111R6X9Xd+ql8DJaZm6JMlB/VUvwYa3Sdn9O+KZ/voxMUkdXtjsSTpmQGN9M3qo3bb9fJ1TXRti5zBYp65Oo1+fqizeZ+WOtQKlaeHQZP+2V3o98LP20Of3tFWD1lcs/p4euiFQY11z9frC7293ILLeeuOTjX1ZL8GNm2/t2ttfb/2qF6+rolu61hTIzrU0KXkNK0+eF5P/7pNkrTk6ayAQtPIYKuBdOkZmUrPNMrP21PlfDy18aUo+Xh6mI/zliPn29UKtdu2Xx/uovm7YrRwd4wei6ovn1yZicuf6WP3Xim0vI9WPddHBoNBRqNRBoNB20/E6bqPVpjXmTS0mV78c4dubltNaw6f1/ELWcemWhX9deR8knm98r5e+ujW1srINOr6VlW181S8PrM4bw9oGqEPb22tDUcuasS0Nebld3etrZvbVdeJi5fVMCJQ/2S384bWVbX20HnNfqy7Qvx9lJFr7oHXhzXXxL93ada2rHOjKRMn2N9b88daD6x6sEcdfbbskO7LDjqHlvfRgrE9tPV4nIa0itSKA+e0ZM8ZjczuZDYYDOYgjWXncWh5H11ITLXa9roX+uo/U1dbvRe5Pdq3vj5YuF+S9MVd7XTfNxvMj/l6eSglPes6uW324Bl/Hy/tnzxQSSkZVh2/7/ynlcZe01A1HATbvD09dHeXWvpp/XEZjVL/puG6tUMNeWR//+Y+0UOJKekK8feRQdLEWbusnu/hYdA/j3ZTanqmKgX4WnW+/jW6m9W6uTukI4L9zNckM0d31ZCPVpofu61jDf2w1vp4fehsosP3S9nHiEYvzVEFf2+V9/UynxO/ubeDRv2wyXxf06FWqBpXCdKSp3vpVOxlpWVkqnalAJtjjrIzHCyvxaIah1sFSXJ/R364v6M616moSynpCi6X83q9LAZ05e5ot2QaNPDOf1rqxMXLal4tWM2rNdcrQ5rK29NDL/25w+Y5Pz7QSWcuJSsxJUOd69qenwJ8vQp9T9emZgUNaRmpZtWCVSPU35zV+uLgxvkeaze/dI0qlPfRhwv36+35+wq8z3phWUFsf4uBAsuf6a3Kgb7y9fJUn0ZhOp+Yos+XH9ZDPeuoQ+2cY5vBYFDDiEA9EdVAIf7e+fZxKfuacmDzKvpixWHtPJX1ufbz9tTfY7rp6PkkNbYIKA1qXkVbx/ez+Qxb2jWxv5qMt3//2CgiZ1u5g6LPZB/vc2tewMBkl3qV7GYdWxpqJwPnlg411KlORYUF+Sotw6jyvl5a9GRPJaZmKCA7o+61G5pr8tBmOnAmQXUrB6hO9vWdl4dBnetW1PL95/Te8Fbmc8/koc304zrr7+2Xd7dX/Rf+lbLvsz09DGpXK1Q3ta2mfu8u1b6YBP05qqt5Tqpb2lfXLxtP6I9HuuhU7GUN/2yNiuLNm1qocZUgfb3qSIHWNRgMahoZpJOxWccNg8GgFtVCtHpcH3V+PaufaNsr/RTk5+2wH9HRgBN7Jg1tRonfK1RsgRxHjMasiwjSpuDIMwMaavOxizp0LucCZVTverq5XTV9tvSQRnSsIS9PD40baDvibPW4PrqcmqHktEzN3HpK93arpQNnEnTrtLV57jOvkcsda4cqOS1DNUP9temlazTw/WWKCC6n57Nv6gY3r2LuIHKVN4Y11+XUDH216oiO5nEh7MieVweo0UtznNI2d+Xv46mkItz0m4zoUEP+Pp66o1NNczDCHkdlGUxKItAzslddDbAYtdixdqjWHr6g5c/0VvVco5IjQ8rpyJTBOpeQotOxyUrPzNT+Mwnq2aCyOr62sED7Cy7nram3t7HqhLa8mfn3se42z8mdodC8arC2n4wr1OssjIbhgdobc8nh45ap3xtejCr09+PmttXMgZzXb2yu2dtPmzuLCuO9W1rJwyC9+OdOnUtIKfTzC2vvpAH6dOkh1QvLuokL8PXSxhej5O3loRavzLP7nEYRgXqoZx35+9heQti7ERzRoYb+2HxSW47H2jxWUKYbR8tOeUklEsjx8fTQ+7e01jVNwjV6+mZ5eRjUqU5FrTiQ9fedNaabDIas65zrWkaaO6FaVg/Ru8NzRtlWCvDV3DxKqQxoVkXzn+ihqhXKmW+GrmsZKS9PD4WW99G6F/pq6pJDeaa7m7Ir2tWqUGyBnKoh5fTOf1rqtdm7tfWE9Xe0V4MwDWtTzTyir6KdmtT/u62NulrcZNnrTH6wRx3zTX/rGhX00a1t1O/dZVL2DX3VkHI2NzOeHgb9+nAXzdsVo4e/32g1uvyjW1ur6dIgtawW4pTrzxEdatjcrCl7NNrD3xf9+G66Zs7tvze10MDmVQo0yjTQz0uX7AQqJKlKsJ9aVg/RvkkDFXc5zdyR98aw5nr2t+1Fbrel/k3DNXdnjM3yOg4C970bhmn+Ez1UPdRf//l0tbZZfMZMr7d9rQq69+usjjwPi79nsL+3gv299ffobnrprx26qW019cge7PNEVAO9uyCnE8XP21O1K5XX0qd7q+sbi1Ql2E83t6uuu7vUMmdydXptoaLjk63at3NCfzXNFdxuUyPEJoD4TP+Gdo9/khQZ7KdTcVnbLcgAhj9HddXQj1fmuc5NbavpmQFZ16WnYpO1fP9ZfTiitfy8Pa0+R96eWW2aNLS5bmpbXS/8sd18PW3ZWWoy9/Ee+nHdMZub/2ZVg9XMItD48a1tNGr6Jn0worXV8utbVVW7WqHq6mCAyIwHO+vNuXs19poGCvH3Md/IN64SZDfTR9mdI7m//88NbKSX/soaIBDo56UXBzfW6bhkvbdgv835vkOtUNWq5K86lQP0cM+6Wn/kgsID/VStQjlzh21mplG3dqxht+a+6XqlSnA582u9sXVVzdsVo+Hta6hyoK8ej6qfb+eZwSAte7q3ery52Oax/nZGcM97oofu/GKdRvWpJ4PBoN0TB2jN4fPqXq+S6mV3DFUM8FHc5bQ89+tI1RB/q8yTTS9llcjy9/HSmD719OGiA0XarrKPhQNyZc+sO3JBzSKDdCExVfXDA/X8oEbmDt7gct4KLuet61v56d8d0epip0PWxMvTQ5ZvdUE6LXPz8fLQ4BZV8szsM30PLcuDyaJPw/Rv82rBuqNTTX23JitId3unmrrdojSV0WhUSnqm3pm/zypQI8kqePf8oMbmx1tWC9bU7OyHznUrmu8dTO0t7+ulhtnZSyue7a2k1Aw1CA80B5eUfY7cPXGAvlhxSD0bhCks0E8f3dpGl1PXa+GeM3ZLbZo8N6CRhraqapUhVS8s0NzZ3LthmHo3zD+DffGTvXTLtDUaZxEYDQv005s3tzQHm+c90UPXfrDCahDTiA7VzcemDrVDtfyZ3hr5w0Y93LOu2tUMVc83F9tkonl7eijY3zog5+FhcBjEMTEYDJrzuP1rM29PD4X4Z13XmEbW5y4j1zSy4JkwfbKzournynprUc36nmjCkKZqWT3EqpPbMoCl7O/MswMa6fk/tuuW9tXl5+2pPa8OkIfBIA+DzMFOZVch+WTJQT0R1UC1srMOPGVQzYp5Z/l8cVd7fbXyiG5uV03VKpRzeI5TdsDIdK1nGcQxeenaJvpt4wmN7lNPX67MGchwR6eaalY1SMPb51zb3tjGuoPXO4/MfnvBG0n677AW+u/cPfrq7g665bPVBQ78vzg4a+CCwWCwKTFnGdw4+JptJtXdXWqpQnZ2/b3dauudBftk77LOz9tDfRuH65/soKrlZeojveuar3dM9+yPReUMEOiVx/fOcj2TqiHldDL2svo3DddjfRtowt87Nap3PfO10ogO1TVj3THz7/4+Xlav0ySvII7peRtejNIN/1upKTe20OI9Z1Q7O7uncqCv/niki93Phcm/j3W3u19nqlXJ+vNvMBjMQRzLZfXDs457L1/XRBP+3qVP72irPo3ClJqRaXX+8fAw6Mu725n/fuV9POXt6eHw2n3WmO46l5BiNXhlyrAWmnh9M/l4eah6qL+qVShnDsoObRWpP7ecshl4kNuiJ3uqTuUA7TplOzjqzZtamO9TP7q1tTrUDlVYYFZAOSbX9a9yfZdNH9OK5X10PjtAP+PBTrol+/osKI+/b263u6BsbVnjtEDOF198oXfffVf792edhOvXr6/HH39c999/v7N2iVIqPMhPi57qZRXdfaRXXRkMhnxTBqsE5xz4mmTffJkORvb8NrKLvD0N5otfe2Y82EnKPnCHlvfR2uet65/2bxZRrIGcWzvW0Kje9dR1yiINbl5FB88m6NiFpDyDDqaLnbu71i5Udo2pNICft6c+v7OdRv+4SbMf7a67vlonH08PHcxntE9p0rpGiDZbdLS8eVNLjZpum6GSH1Mnpr1RqF/e3U5NI4N1xxdrzSOWDAaDgvy87I74laR+JVAneew1DWQwGPTGsOa6lJyu+7vXUUamMc8LcMuRZK1rFHxEhbJvUi07EmaO7qrfNp6wu+7yZ3rr6Pkktcm1jwHNIoocyPH2NCgtw34HqMm/j3U3j6SpWN5Hd3SuqRtbVzN3qFiWJPDz9lSIv7dis0cNj+5dTx8ttu7QeP3G5hr3e07HZ1iQn27tWENeHgaN6FBDFxJTCxzI+e6+Dlqx/5yGt69u7uzs3zRCKw+c1+1f5B2UvlK+Xp5W9YOVR6kAZXdgv36j43rJY/rUM3eumQIOPl4e+nNU10Idq/ZNGqjbv1irdYcvaJFFCb7i7pT/5t4OuuvLdXmus/b5rHJcg5tXUfm7s25wUtIz9MgPm/RQz7rmjkBlf3aOTBmslPSMQnUu3dgma7SY6WbBnrBAP42/Lu9zojOYRjf/Nbqb+W9oKonq4WHQ2//JGRhxf/c6mrXttAY0jdDHt7XR2UspNlkwzw5opF82HFedygFasDurw9+yk0eSGoQH6u/R3RQe5KuwoKznb3+ln75aeURDLEZme3gYNKBZhE2Az2Aw6JFezkvZf/3G5nrthmaa8PcubTh6wZxJ4mtnnoDCsFfSqE7l8vpPu+p217cnr1KGpmsfHy8Pq3kRbmxTTc//scNmBHdR9G2cE8iZNaab3luwT7e0tx98NNUkN33ufxvZxTxy0VIni/Ovh53zWIXyPvoo15w59uZsMK27efw1ViP4TRY/1UuNx1sH8e2dN7+6p4NaTrAOdJs6BJ4Z0FD/nWOdnXl/9zq6tWMNh6X/LC15qpdV50LuYMtvIzvr762n9ZTFnBAje9U1j4xXruOk5felVfUQq5ryo3rXVeVAX/WsX1nVQ8vpclqG/H289MLgxmpeNdgqAJvb4BZV1K/pQLsdbFVDyunA5IG6/9sNalwlyCqjqmFEoD6/q3B1+zvWqagjUwbr8LlE9c4eSNPEIuCycGxPhQX5KTPTqGuahKtheKA50CFJlYN89d+bco5T7e1kaXhkB+gL6u3/tFRahtHcoeLoeF8luJwahAfI18tT5bw9VaOiv7aO76fxM3fohuxgULUK5ey+jw3CA7Xm+ZxykOV8PM2d50uf7qW0DKMC/bxtMh7y89ODnRQdn2w+Hix6sqc8PQxWcwT0aRRW5EBOl7oVrYI4prabAiKmawwvO6/Zx8tDX97dvkj7LW6tqofYnFsceSyqvjmQk5vBYJCft6eeiGqg8j5e6t/M8YCvz+5oq48XH7AaBKLse4eVz/VRRJDtPW61CjmBitxtLefjaZUlJkkf39ZGO0/Fm0eA2+PhYbD6jhVVsL+33cFc7WuF6rM72qp2pfKqHx6onx7qpJdn7tSQlpHq2zhcEUF+6lQnVN6eHgrw9VKgn7dmjcnZzt5JA2226Wz1wwO18Mme+ZY8ykt4kJ+2ju8nf1/H54IJQ5rKy9NDln/JhuGBerBHHT35y1bzsnlP9FB4kJ96NKikyOz+EMtzjOUhqVFEkN6/pXWh21s91D/P6863bm6pF/7Yrql3tLUJeOZ2X7fa5gwv02CYZwc0sjp3Faf/tK+um9tVk8Fg0B+juuqer9absw3saRQR6DCgZ9KlbkVNvqGZGoYHmq8NTP0NHgbrMpHlfb20a8IAq2uKFwc31qfLDumnBzupTuUA/bMt65o6wGJgXK8GYbqhdVW7gwqK4reRXfTXlpMa3r66Qvx99NNDna0e9/fxssnIK6pKAb5a/kzWPUPu6wdHfQtf3dNep2OTSzyIUxT3dK2tER1yruXsnff7NArXhhejNG3ZIQ1vn/e1u4+Xh1UQx3K5ieV3esqwFurfNCLfDCRTX0LuU5enh0GWV/mWAwkk2QzWU/bn4+Nb2yg9M1OB2dl0r93YXA99t1G+Xh7qWDtU17eKVHA57zwDrsuf6a1qFcrp5w3HbUrHomicEsgZP3683nnnHY0ZM0adO2cdLFavXq0nnnhCx44d08SJE52xW5QhV9pZZ9mxu+jJnpq9/bTu6lLLfABypEOt0Hz3fSCPUf25dalbUaHlfdSzQWVz9Du35lWDVTWknPa8OkC+Xh4yGrMOvPZKKBSVKRXSUlSTcO15NetCePGTvZRhNKrhi2UnS8cj199xcIsqGjW9YM+1HG39+V3tHF5cNKkSrPAgP819vIeG/m+VameP+lr0VC/tPh2vO77I6SAO9PXSRw7qkRcny1EflqOb8griXKnOdSvp5w3Hzb9X8PfRmL71tebQBd3cznpUVfVQf6usoE9ua6N5u2J0b9faRS5NtmviALudf5YsO//OJ6bq8ex637Mf7a7Fe8+Yby5MNrwQpddm71H3+pXUu1GYHupZR80tMlRGdKhhFchRrgkh7+9eW4fPJepXBwEtS93rV7aZp8JgMKhBRPGWnutQK1QHziboQmKq7utW2+7ILUthgb46c8k6K2hSPjWqb+9UU53rVlStiuVtOmksSzrlx8fLQz892Ekp6ZkF6vwsKssbz//e1MJq9OOAphF6f0Qr84W6wWBQb4sa87kn2bRU0CCOn7eHktMybeppm8pM9XDx/CW5XdMkXPN3xVjNmWGpVfUQbRmfVSLNYDDYLWVm6nSetzNaC3bHqEmVILvn3dxlFQL9vG2CjibOzvr29fLQF3e11yt/79SYPvXM+3xlSFNlZBr16qxd6lA7VJ1qVzSXkjNpGhkkHy8Pq4EFjjSNDLbJ6ilsDenqof7q1zTCbsaQvSw6ZY92/eS2NlecLfrNvR3U0CIYWatSeX1+l+NO2dR06zKS3p4e+vzOdnrhz+3m+uiyGOnp7VHwQFleISlH389yduY+yX0toewRivOf6KFrsrPGZDFPxiO96um2DjXVcmLW+WJEh+q6s3NNu53Wyg7UmUrmjBvYyBzE+W1kVlmN61pGWgVy2tYMVdua9stFWfptZBedvZScZ3DY18vTasSx6fPh7emhYQUoeZHXTbuXp4e+vid78JCXp95dsO+KJ4q3nJskOS3ns2M6v3t4GGxGx1cK8NHL+QwKKwqDwSAfr/yPO54eBv37WA8ZLI5Twf7e5k7VvEZX58VyJL2j6zvT+SW33AOT7GXLtaoeoge611bNiuX1op1yRva0q1lBG45e1C125gkorQp6bqkU4KtdE/vnee4v5+OZ73VXv6YRDgd8FWUidHv8vD3NJclcyfJ1tq5RQTNzlSL78YGcgZXuojhKQjvKbPhrVFetPHjOPCdMmEXQ7o9RXVTO29McyLmnay2FZz9uGcgraTe1raahrSIdnt8cMQ2GKezf1lSesKClQk3bbxAeaB6UtOtUvJ76Zav2n7lkNQgwvywH0/ZMc7OYfHlXe83dGa1BLarY3K+U8/HUjgn9Nfmf3Rrdp56qhpTTfd1qm9t1Y5uq+n3TSY3uk3Od55E9h0txiQj200M9nRMsKw4FyexzJwW5J60U4Ktxg4o2d11ulQN8zaVl/bw9NTDXvGh5DR62zC4a0aG6HulVT/vPOO7HfG94Kz3+k21p3NyZq/2ahOv7+zqqQUSADAaDVZDYMoNo1phuuvPLdXqkV11z389wBwO7UHhOCeR88sknmjZtmkaMGGFeNmTIELVo0UJjxowhkAOne7hnXX246ICqhpRTncoBNqORcpv9aHfNWH9MY/JZz5E+jcLUKCJQ/2lXXeNn7jTP7ePt6WEeJdq5bkUN/3SN7u5SS5Nn59RXNU2eZjox2LumubNzTX2bXVc7rxthUwdS7rIS9joiLHl5esjTQUmXknZT22qasyPaXM/WYJDdtOS8TL+/ozw8DOZUz+n3dyzU81+/sbm5E8zeCXvhkz11KTnd3ElpMBisJhWtFOCr7vUrm8uFta9VQT892NnuSGJ3Fh7kq5j4nI780PI++uH+jrr987V6PKq+hrSqqvMJKapTOUBhFqO7K5T3UYCvV55lpEwGNq9ic1FSWN6eHub5U0z6Nw1Xs8hgvT1/n1XHYm5NIoPsjjr08vSwGoUW6OctHy8Pq47Ht29uqad+3Wp3cj9fL0+9dXNLXU7NsMrgm/5Ax3xLP5qEBfrpq3vay9/bs8g1ci3VCw/Ql/e017YTsepYu2K+wb3lz/ZWSnqmHvp2o1YfOm8uv5YXg8FgLr2RW8vqIToweaDVSOncRvWua860NI1idRbTxLHbXumnlLRMVQ70VZsaIYp6J6tz9s7ONYtUsqUwVjzbR/tjEtSpjnXH7F+juiklPcNhx7szhAf5qnoFf/3ycGcdPJuoqHeWKvef+9Pb2+piUmqeWVumMiT5uaZJuP59rLtq5VPawx0Ma1tN3epX0gI7oxY9PQxWE71ueDFKb8/bp6lLs7IQ/nm0uyb8vdMcyIkM9lP/ZhH6aqX9utUDmkVYzQmw+7RtaYTcLEujGbPPYa8MaaJ90Qmatf2U6lUOsFsjvDjNHN1VLaqFyGg0KqpxuHy8bEtU5JaSbpt5HNUk3O4EvYUdBX1D66qa8u+efEcJ58fLw6C7u9SyKTVWPzxQP9zfUbd9nnU8z8rOyPrCBJXzUtd6FZWclqnXbmieZ2fVtc2r6O9tp3X4XKKub5XzN2pbs4K5szV3cLAg3KGj1uTRvvV0Q+uqNhPIF5avRUdbBYvjTO652ZTdIXEy9rLVBPau4syBNMruyH2kV119ufKwOXBTs6K/Fj3ZS5uPXdR7C/bLy9OgJXvP5rstE4PBoBcGZ10DFTSQ8/39HXX4XKJVOa6rSUmer68G7hTAKQktq4dYlZ3uUb+SHu1TT00ig8yfrZ0T+uvMpRSrSdldrbBBHJOi/H3/HNVVW0/EqVu9ShrUrIpVsKugmkQGafZj3TXy+436d0e0efmzAxrl+TxHKpT3yTN4HeDrZVXJwPJ1/3dYC93frY4aV7k6j5nI339vaqHHf9qiB7rbH0D368gu5lLUyq5oYVI91F8je9VVgK+X+VqoWoVyGtmrrprbmZNzaOuqal871KpPxx6DwaBu9e1nBS1/prcem7FF4UG+alY1WBtfjLrqjuUlxSlXHGlpaWrXzjZtvm3btkpPL9xkY0BRjOlTX/XCAhzWTc2tSWSQJjqozZ1bz4aV9UGucgMfjmit8tkdFt/e28FcfsbyuFWtgr95NMidXWqas18KUsrhleuaqnOdikpOz7BJg7T0+yNdNG3ZIT3Sq55VDe6C3EO6y0H2rZtb6ql+DdXp9az5WepUKl+gkm8PdK+tYW2rqW7lAPMo0R0T+qu8j2eRXtvE65vqnIOL5YKOyPr+vo5afuCsohqHFzmIs/yZ3ooMKaeZW0/qlZm71KxqkFYeOF+AZ165v0Z10+jpm7Tp2EV1r19ZL1/XRHUqB2iDxUnZVDvVMjU4v847Z2heLViHXx9kzmTz8/bUmL71dWPbaqpkZ96Ooph0fTM989s28wj5YW2raUiryDxHJY/t18AqkFO7Unk1igjUnuiCZfYVdaTSkSmDZTQalZqRaT7WVK/grwBfL3Wpm3dKtomvl6d8vTz10a2t9eO6Y7qpbcHLOzmS1w3fl3e3U59Gec8xdaUOvz5IS/ad1cmLl82TJwf5eUvZ94L1wgK1d9IAxcSl5FtXvTjknhzXxNPDUOKdQh/d2sZccqheWEBWCZFc6e8eHoY8gziFYTAYSkUpBeUxd4093p4eGtAsQlOXHjRnEFg+feVzffSFxUT3g5pHaPb2aKtt9LWYs8tRHem7u9TSjPXHtGBsT1Wr4J8zx032zny9PLMnCi54/f7CGNGhuubujDGXdTLV+DcYDA5LZ815vLsW7j5jzr60LI9b3MKD/LR74gCHJdYciQjyM8+Ts/HFKHl4GPTC4MaasyPaZv6cLnUramCzCPl5e1qN5jUYDPr+vo7m/+dn7uNZE2pXKG//XNWiWnCR5lxzFwZD/vNUFHQ7i57sqeS0TAX65Rwf7V1fOTtw6W6eGdBIzwxopCPnEvXZ8kN6qEcd86TK32cPZjoTnyw/O1lnV8KUUdamRoj8vD1LzTEdcHcGg0Fj+zW0Wlbe10u1XXCP5S5C/H3MgzPyKy+Vn3EDG2vD0Yu6p2st3dGpZr5VW5zBy9OjWEoYouyqHuqv30Z2cfh4g/DArGoIx2P1dP+GNplluQOUBoMhz6DllWZ+GgwGfTCitdXvcA6nnAnuuOMOffLJJ3rnnXesln/22We67bbbnLFLlAG3tK+uGeuPq0Pt/EtG5MfHy8NqZGNxyj23h7IvrCyN6VNPny47ZJ5QNjdfL09te6Wf4pLSbCafz238tU3k4WFwmLVQPyxA+88kqHv9SqpbOUB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},
"metadata": {},
"output_type": "display_data",
"jetTransient": {
"display_id": null
}
}
],
"execution_count": 54
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Step 3: Testing the microphone array\n",
"Below are some experiments you can perform to test the microphone array, and develop the code.\n",
"\n",
"**Part 1: Clapping**: With the real microphones connected, have your teammate clap in front of each microphone in turn. Record the audio data and plot the results. Is the order of the microphones what you expected? How does the sound intensity change as you move from one microphone to another?\n",
"\n",
"**Part 2: Beacon detection**: Turn on KITT's beacon and record the results. Can you identify where KITT is located just by observing the shift in the recordings? Change the beacons parameters and see how it affects the recordings.\n",
"\n",
"**Part 3: Ideal OOK signal**: Compare the waveform of the recording to an ideal OOK of your code. What can you see, and what do you infer from this? Are some beacon signals better than others? How can you find a good beacon signal? (This point is revisited in Module 3.)\n",
"\n",
"**Part 4: Reference recording**: Make some recordings of the beacon at different locations. These recordings will be useful to your teammates working on the localization algorithm. Similarly, make a recording of a single pulse from the beacon close to one of the microphones. Cut out the pulse and save it separately.\n",
"\n",
"**Part 5: KITT class**: Add a method to read the microphones to your KITT class in 'Student Code' files. The method should make a stream, turn on the beacon, start the recording, stop the recording, and turn off the beacon. You can choose to return the recording as a result, or store it internally inside the KITT class. Make sure to test your code."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"*Bonus Tasks - Optional*\n",
"\n",
"- See if you can automate selecting the correct sounddevice device index. The correct device index changes from one computer to another and can sometimes even change on the same computer after a reboot. So, it is worth your time to make a program that can automatically select the right device index.\n",
"- Implement start-up sanity checks: some process which you can run after you arrive at the test field, so that you can quickly check the microphone connections and audio levels.\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Mid-term assessment 2.2 and report\n",
"\n",
"After you finish this assignment, and ultimo in week 4, showcase the functionality of your script to your\n",
"assigned TA. After you pass this assessment, you are ready to document your results in your midterm\n",
"report.\n",
"\n",
"For this Module, you would include a chapter that covers the above tasks (using independently-readable\n",
"text, i.e., dont refer to “Task 1”). Include plots; for each plot it should be clear how the plot was made\n",
"(i.e., the corresponding experimental set-up), and you have to describe what is seen in the plot before\n",
"you discuss results and derive any conclusions. Be sure to answer the questions posed along with the\n",
"plots (using independently-readable text).\n",
"\n",
"Include the corresponding code in an Appendix. Remember to document your code, using comments\n",
"to define input/output variables of functions and to explain the logic and any modifications made. Your\n",
"completed script will be crucial for the upcoming challenges, contributing to the overall autonomous\n",
"driving system.\n",
"\n",
"This concludes the mid-term assignments related to communication with KITT. After the mid-term, you\n",
"must integrate this module with the localization module created by your colleagues. Take into account\n",
"that integrating is often harder than originally anticipated, e.g. your code has to run in parallel, and you\n",
"have to worry about timing aspects. Hopefully, using the KITT class will provide you with a sturdy and\n",
"flexible framework to continue your work towards the final challenge\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## FAQ\n",
"\n",
"**What is the beam angle ?**\n",
"\n",
"The beam angle of a sensor refers to how wide the sensor's detection area is. It determines how much space the sensor can cover when it sends out signals (like sound or light) to detect objects.\n",
"\n",
"To determine the beam angle of ultrasonic sensors mounted in front of the car, you have multiple options: \n",
"\n",
"1. **Check the sensor datasheet**: The easiest way or at least a way to get some idea to determine the beam angle is to refer to the manufacturer's datasheet for your specific ultrasonic sensor. The datasheet will typically provide the beam angle, often around 15 to 30 degrees for common ultrasonic sensors. But keep in mind that is for a single sensor and not the current set up! Also, the 'reach' of the sensor is angle dependent: straight ahead, it can see several meters, but at an angle, perhaps just half a meter. \n",
"\n",
"2. **Experimental Determination for KITT**:\n",
" - **Measure detection width**: Place a flat object (like a wall) at a fixed distance in front of the sensor (e.g., 1 meter).\n",
" - **Move the object**: Move the object left and right to determine the points where the sensor stops detecting the object.\n",
" - **Calculate the angle**: Measure the distance between these two points (detection width) and the distance from the sensor to the object. You may use the following formula:\n",
"\n",
" \n",
" $$\n",
" \\text{Beam Angle} = 2 \\times \\arctan\\left(\\frac{\\text{Detection Width}/2}{\\text{Distance to Object}}\\right)$$\n",
" \n",
" - This calculation will give you the beam angle in degrees.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**I see random numbers from sensors for large distances. Is my sensor damaged ?**\n",
"\n",
"During experiments, you may occasionally receive random or unexpected data from the sensors. This can occur not only when the sensors are operating outside their effective range but also at times when they are within range. Several factors (consider what they might be?) can cause ultrasonic sensors to produce inaccurate readings. Additionally, since there are two sensors—one on the left and one on the right—they might produce different, completly different readings.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Are the ultrasonic sensor measurements for the left and the right side done at exactly the same time ?**\n",
"\n",
"If you closely observe the blinking of the small LEDs on the ultrasonic board on the car, you might notice that they turn on and off alternatingly. This indicates a slight time difference in the sensor measurements. This delay is also noticeable and can be measured using a moving car. (The reason to operate the two sensors alternatingly is that otherwise they might interfere on each other.)"
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