diff --git a/Arduino_Code/LCD_Test/LCD_Test.ino b/Arduino_Code/LCD_Test/LCD_Test.ino
new file mode 100644
index 0000000000000000000000000000000000000000..b7a292107687ec85741560a0ef4ed682e55fdf7e
--- /dev/null
+++ b/Arduino_Code/LCD_Test/LCD_Test.ino
@@ -0,0 +1,68 @@
+
+#include <LiquidCrystal.h>
+// initialize the library by associating any needed LCD interface pin
+// with the arduino pin number it is connected to
+const int rs = 12, en = 11, d4 = 5, d5 = 4, d6 = 3, d7 = 2;
+LiquidCrystal lcd(rs, en, d4, d5, d6, d7);
+int incomingByte = 0;
+void setup() {
+ Serial.begin(19200); //Setup the baud rate. The amount of times it samples per second.
+ // set up the LCD's number of columns and rows:
+ lcd.begin(16, 2);
+ // Print a message to the LCD.
+ lcd.print("Prediction:");
+}
+
+void loop() {
+ if (Serial.available() > 0)
+ {
+ // read the incoming byte:
+ incomingByte = Serial.read();
+ // say what you got:
+ Serial.print("I received: ");
+ Serial.println(incomingByte, DEC);
+ }
+ // set the cursor to column 0, line 1
+ // (note: line 1 is the second row, since counting begins with 0):
+ lcd.setCursor(0, 1);
+ if(incomingByte == 57)
+ {
+ lcd.print("9");
+ }
+ else if(incomingByte == 56)
+ {
+ lcd.print("8");
+ }
+ else if(incomingByte == 55)
+ {
+ lcd.print("7");
+ }
+ else if(incomingByte == 54)
+ {
+ lcd.print("6");
+ }
+ else if(incomingByte == 53)
+ {
+ lcd.print("5");
+ }
+ else if(incomingByte == 52)
+ {
+ lcd.print("4");
+ }
+ else if(incomingByte == 51)
+ {
+ lcd.print("3");
+ }
+ else if(incomingByte == 50)
+ {
+ lcd.print("2");
+ }
+ else if(incomingByte == 49)
+ {
+ lcd.print("1");
+ }
+ else if(incomingByte == 48)
+ {
+ lcd.print("0");
+ }
+}
diff --git a/Arduino_Code/testing.ino b/Arduino_Code/testing.ino
new file mode 100644
index 0000000000000000000000000000000000000000..3de3aed7f0992f45d7f300b6b45688d4ba3d56ab
--- /dev/null
+++ b/Arduino_Code/testing.ino
@@ -0,0 +1,103 @@
+int incomingByte = 0;
+
+void setup() {
+ Serial.begin(19200);
+ pinMode(13,OUTPUT);
+ pinMode(12,OUTPUT);
+ pinMode(11,OUTPUT);
+ pinMode(10,OUTPUT);
+ pinMode(9,OUTPUT);
+ pinMode(8,OUTPUT);
+ pinMode(7,OUTPUT);
+ pinMode(6,OUTPUT);
+ pinMode(5,OUTPUT);
+ pinMode(4,OUTPUT);
+}
+
+void loop() {
+ // send data only when you receive data:
+ if (Serial.available() > 0) {
+ // read the incoming byte:
+ incomingByte = Serial.read();
+ // say what you got:
+ Serial.print("I received: ");
+ Serial.println(incomingByte, DEC);
+ }
+ if(incomingByte == 57)
+ {
+ digitalWrite(13,HIGH);
+ delay(1000);
+ digitalWrite(13,LOW);
+ delay(1000);
+ }
+ else if(incomingByte == 56)
+ {
+ digitalWrite(12,HIGH);
+ delay(1000);
+ digitalWrite(12,LOW);
+ delay(1000);
+ }
+ else if(incomingByte == 55)
+ {
+ digitalWrite(11,HIGH);
+ delay(1000);
+ digitalWrite(11,LOW);
+ delay(1000);
+ }
+
+ else if(incomingByte == 54)
+ {
+ digitalWrite(10,HIGH);
+ delay(1000);
+ digitalWrite(10,LOW);
+ delay(1000);
+ }
+
+ else if(incomingByte == 53)
+ {
+ digitalWrite(9,HIGH);
+ delay(1000);
+ digitalWrite(9,LOW);
+ delay(1000);
+ }
+
+ else if(incomingByte == 52)
+ {
+ digitalWrite(8,HIGH);
+ delay(1000);
+ digitalWrite(8,LOW);
+ delay(1000);
+ }
+
+ else if(incomingByte == 51)
+ {
+ digitalWrite(7,HIGH);
+ delay(1000);
+ digitalWrite(7,LOW);
+ delay(1000);
+ }
+
+ else if(incomingByte == 50)
+ {
+ digitalWrite(6,HIGH);
+ delay(1000);
+ digitalWrite(6,LOW);
+ delay(1000);
+ }
+
+ else if(incomingByte == 49)
+ {
+ digitalWrite(5,HIGH);
+ delay(1000);
+ digitalWrite(5,LOW);
+ delay(1000);
+ }
+
+ else if(incomingByte == 48)
+ {
+ digitalWrite(4,HIGH);
+ delay(1000);
+ digitalWrite(4,LOW);
+ delay(1000);
+ }
+}
diff --git a/MNIST_Code/keras_training.py b/MNIST_Code/keras_training.py
new file mode 100644
index 0000000000000000000000000000000000000000..09c08ee7dc80926bdcb3c6f41a7e64929f7b9a89
--- /dev/null
+++ b/MNIST_Code/keras_training.py
@@ -0,0 +1,104 @@
+import tensorflow as tf
+from tensorflow import keras
+import numpy as np
+import matplotlib.pyplot as plt
+import serial
+import time
+ser = serial.Serial(
+ port='/dev/ttyACM0',
+ baudrate=19200,
+ parity=serial.PARITY_ODD,
+ stopbits=serial.STOPBITS_TWO,
+ bytesize=serial.SEVENBITS
+)
+plt.rcParams['interactive'] == True
+fashion_mnist = keras.datasets.fashion_mnist
+(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()
+class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',
+ 'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']
+train_images = train_images / 255.0
+test_images = test_images / 255.0
+plt.figure(figsize=(10,10))
+for i in range(25):
+ plt.subplot(5,5,i+1)
+ plt.xticks([])
+ plt.yticks([])
+ plt.grid(False)
+ plt.imshow(train_images[i], cmap=plt.cm.binary)
+ plt.xlabel(class_names[train_labels[i]])
+plt.show()
+model = keras.Sequential([
+ keras.layers.Flatten(input_shape=(28, 28)), #Transforms the format of the images from a 2d-array (of 28 by 28 pixels), to a 1d-array of 28 * 28 = 784 pixels
+ keras.layers.Dense(128, activation=tf.nn.relu), #This is the fully connected layers
+ keras.layers.Dense(10, activation=tf.nn.softmax) #Output layer
+])
+model.compile(optimizer=tf.train.AdamOptimizer(),
+ loss='sparse_categorical_crossentropy',
+ metrics=['accuracy'])
+model.fit(train_images, train_labels, epochs=5)
+test_loss, test_acc = model.evaluate(test_images, test_labels)
+
+print('Test accuracy:', test_acc)
+predictions = model.predict(test_images)
+np.argmax(predictions[0])
+def plot_image(i, predictions_array, true_label, img):
+ predictions_array, true_label, img = predictions_array[i], true_label[i], img[i]
+ plt.grid(False)
+ plt.xticks([])
+ plt.yticks([])
+
+ plt.imshow(img, cmap=plt.cm.binary)
+
+ predicted_label = np.argmax(predictions_array)
+ if predicted_label == true_label:
+ color = 'blue'
+ else:
+ color = 'red'
+
+ plt.xlabel("{} {:2.0f}% ({})".format(class_names[predicted_label],
+ 100*np.max(predictions_array),
+ class_names[true_label]),
+ color=color)
+
+def plot_value_array(i, predictions_array, true_label):
+ predictions_array, true_label = predictions_array[i], true_label[i]
+ plt.grid(False)
+ plt.xticks([])
+ plt.yticks([])
+ thisplot = plt.bar(range(10), predictions_array, color="#777777")
+ plt.ylim([0, 1])
+ predicted_label = np.argmax(predictions_array)
+
+ thisplot[predicted_label].set_color('red')
+ thisplot[true_label].set_color('blue')
+i = 0
+plt.figure(figsize=(6,3))
+plt.subplot(1,2,1)
+plot_image(i, predictions, test_labels, test_images)
+plt.subplot(1,2,2)
+plot_value_array(i, predictions, test_labels)
+plt.show()
+num_rows = 5
+num_cols = 3
+num_images = num_rows*num_cols
+plt.figure(figsize=(2*2*num_cols, 2*num_rows))
+for i in range(num_images):
+ plt.subplot(num_rows, 2*num_cols, 2*i+1)
+ plot_image(i, predictions, test_labels, test_images)
+ plt.subplot(num_rows, 2*num_cols, 2*i+2)
+ plot_value_array(i, predictions, test_labels)
+# Single image testing
+img = test_images[0]
+print(img.shape)
+img = (np.expand_dims(img,0))
+print(img.shape)
+predictions_single = model.predict(img)
+print(predictions_single)
+print(np.argmax(predictions_single[0]))
+for i in range(0,len(test_images)):
+ img = test_images[i]
+ img = (np.expand_dims(img,0))
+ predictions_single = model.predict(img)
+ predict = int(np.argmax(predictions_single[0]))
+ time.sleep(3)
+ ser.write(b'%d' % predict)
\ No newline at end of file
diff --git a/MNIST_Code/keras_training_mnist.py b/MNIST_Code/keras_training_mnist.py
new file mode 100644
index 0000000000000000000000000000000000000000..56c657a8a19653cdbe6ee0aa6de3409b38ed4b04
--- /dev/null
+++ b/MNIST_Code/keras_training_mnist.py
@@ -0,0 +1,103 @@
+import tensorflow as tf
+from tensorflow import keras
+import numpy as np
+import matplotlib.pyplot as plt
+import serial
+import time
+ser = serial.Serial(
+ port='/dev/ttyACM0',
+ baudrate=19200,
+ parity=serial.PARITY_ODD,
+ stopbits=serial.STOPBITS_TWO,
+ bytesize=serial.SEVENBITS
+)
+plt.rcParams['interactive'] == True
+mnist = keras.datasets.mnist
+(train_images, train_labels), (test_images, test_labels) = mnist.load_data()
+class_names = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']
+train_images = train_images / 255.0
+test_images = test_images / 255.0
+plt.figure(figsize=(10,10))
+for i in range(25):
+ plt.subplot(5,5,i+1)
+ plt.xticks([])
+ plt.yticks([])
+ plt.grid(False)
+ plt.imshow(train_images[i], cmap=plt.cm.binary)
+ plt.xlabel(class_names[train_labels[i]])
+plt.show()
+model = keras.Sequential([
+ keras.layers.Flatten(input_shape=(28, 28)), #Transforms the format of the images from a 2d-array (of 28 by 28 pixels), to a 1d-array of 28 * 28 = 784 pixels
+ keras.layers.Dense(128, activation=tf.nn.relu), #This is the fully connected layers
+ keras.layers.Dense(10, activation=tf.nn.softmax) #Output layer
+])
+model.compile(optimizer=tf.train.AdamOptimizer(),
+ loss='sparse_categorical_crossentropy',
+ metrics=['accuracy'])
+model.fit(train_images, train_labels, epochs=5)
+test_loss, test_acc = model.evaluate(test_images, test_labels)
+
+print('Test accuracy:', test_acc)
+predictions = model.predict(test_images)
+np.argmax(predictions[0])
+def plot_image(i, predictions_array, true_label, img):
+ predictions_array, true_label, img = predictions_array[i], true_label[i], img[i]
+ plt.grid(False)
+ plt.xticks([])
+ plt.yticks([])
+
+ plt.imshow(img, cmap=plt.cm.binary)
+
+ predicted_label = np.argmax(predictions_array)
+ if predicted_label == true_label:
+ color = 'blue'
+ else:
+ color = 'red'
+
+ plt.xlabel("{} {:2.0f}% ({})".format(class_names[predicted_label],
+ 100*np.max(predictions_array),
+ class_names[true_label]),
+ color=color)
+
+def plot_value_array(i, predictions_array, true_label):
+ predictions_array, true_label = predictions_array[i], true_label[i]
+ plt.grid(False)
+ plt.xticks([])
+ plt.yticks([])
+ thisplot = plt.bar(range(10), predictions_array, color="#777777")
+ plt.ylim([0, 1])
+ predicted_label = np.argmax(predictions_array)
+
+ thisplot[predicted_label].set_color('red')
+ thisplot[true_label].set_color('blue')
+i = 0
+plt.figure(figsize=(6,3))
+plt.subplot(1,2,1)
+plot_image(i, predictions, test_labels, test_images)
+plt.subplot(1,2,2)
+plot_value_array(i, predictions, test_labels)
+plt.show()
+num_rows = 5
+num_cols = 3
+num_images = num_rows*num_cols
+plt.figure(figsize=(2*2*num_cols, 2*num_rows))
+for i in range(num_images):
+ plt.subplot(num_rows, 2*num_cols, 2*i+1)
+ plot_image(i, predictions, test_labels, test_images)
+ plt.subplot(num_rows, 2*num_cols, 2*i+2)
+ plot_value_array(i, predictions, test_labels)
+# Single image testing
+img = test_images[0]
+print(img.shape)
+img = (np.expand_dims(img,0))
+print(img.shape)
+predictions_single = model.predict(img)
+print(predictions_single)
+print(np.argmax(predictions_single[0]))
+for i in range(0,len(test_images)):
+ img = test_images[i]
+ img = (np.expand_dims(img,0))
+ predictions_single = model.predict(img)
+ predict = int(np.argmax(predictions_single[0]))
+ time.sleep(2)
+ ser.write(b'%d' % predict)
diff --git a/README.md b/README.md
index 76e579ae4c9106f3b62fb9203ec5b49d8014d87c..e9a3884db16c38830b4fd665eff6870ad66ce249 100644
--- a/README.md
+++ b/README.md
@@ -1,2 +1,2 @@
-test
-
+# Senior Design
+This project's purpose is to explore machine learning algorithms for use of autonomous vehicles.