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// import * as tf from "@tensorflow/tfjs-node";
import * as tf from "@tensorflow/tfjs";
// Model architecture
export function pricePredictionModel() {
  // Clean all variable stored in the engine - otherwise variable already registered error will occour
  // tf.disposeVariables()
  // Apartment size input
  const sizeInput = tf.input({shape: [1], name: 'size'});
  const denseSize = tf.layers.dense({units: 2, name: 'denseSize1'}).apply(sizeInput);

Ngugi1

export function qualityOfStay() {
  // Clean all variable stored in the engine - otherwise variable already registered error will occour
  // tf.disposeVariables()
  // let inputs = ['size', 'latitude', 'longitude', ]
  const sizeInput = tf.input({shape: [1], name: 'size'});
  const denseSize = tf.layers.dense({units: 2, name: 'denseSize'}).apply(sizeInput);

  // latitude input
  const latitude = tf.input({shape: [1], name: 'latitude'});
  const denseLatitude = tf.layers.dense({units: 2, name: 'denseLatitude'}).apply(latitude);

Ngugi1

  <div class="container">
    <video class="input_video"></video>
    <div class="canvas-container">
    <canvas class="output_canvas" width="1280px" height="720px">
    </canvas>
    </div>
    <div class="loading">
      <div class="spinner"></div>
      <div class="message">
        Loading

Ngugi1

// How to run this code
// 1. Connect the Ardurino UNO to the JETSON NANO via USB Cable
// 2. Connect the step motor driver to pins 8-11 on the Ardurino UNO
// 3. Connect the step motor to the motor driver 
// 4. Supply power to the motor driver by connecting it to the 5v and ground pins on the ardurino
// 5. Connect the step swith to the pin 2 of the ardurino and the other pin to GND
// 6. Install Ardurino studio on the JETSON NANO
// 7. Create a new file and copy this code to the newly created file
// 8. Deploy this code to the ardurino UNO
// 9. To simulate a closed BIN, close the circuit using the step switch

Ngugi1

# This code is based on work from this website - https://www.pyimagesearch.com/2018/03/19/reading-#barcodes-with-python-and-openmv/ - and modified to fit our purpose

# ## How to run this project
# Ensure that you `python3 installed`

# Make sure that all these dependencies are installed on the JETSON NANO which is running linux using the commands provided below.

# 1. `sudo apt-get install libzbar0` - zbar library is used together with openCV to facilitate reading of barcodes 
# 2. Install python module `pyzbar` using command `pip install pyzbar`
# 3. Install python module `imutils` that is used to ease the processing of images using command `pip install imutils`

Ngugi1

#include <Stepper.h>

// Stop switch
int stepswitch = 2; 
// Motor
const int stepsPerRevolution = 480;  // change this to fit the number of steps per revolution
int motorSpeed = 100;
// initialize the stepper library on pins 8 through 11:
int stepCount = 0;  // number of steps the motor has taken
int motorPin8 = 8;

Ngugi1

#include <Stepper.h>

// Stop switch
int stepswitch = 2; 
// Motor
const int stepsPerRevolution = 480;  // change this to fit the number of steps per revolution
int motorSpeed = 100;
// initialize the stepper library on pins 8 through 11:
int stepCount = 0;  // number of steps the motor has taken
int motorPin8 = 8;

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int Lswitch = 2; 
int led = 13; 
int flag = 0; 
 
void setup()
{
  Serial.begin(9600); 
  pinMode(Lswitch, INPUT); 
  pinMode(led, OUTPUT); 

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/*
 Stepper Motor Control - speed control

 This program drives a unipolar or bipolar stepper motor.
 The motor is attached to digital pins 8 - 11 of the Arduino.
 A potentiometer is connected to analog input 0.

 The motor will rotate in a clockwise direction. The higher the potentiometer value,
 the faster the motor speed. Because setSpeed() sets the delay between steps,

Ngugi1

from serial import Serial
channel = serial.Serial('/dev/cu.usbserial-1420',9600)
def led_on():
    channel.write('1')
def led_off():
    channel.write('0')
led_on()