Overview
How it works
Setup development environment
Add SparkFun Micro OLED Breakout board
Add SparkFun Environmental Combo Breakout - CCS811/BME280 board
The Model
The Code
Add SparkFun Proximity Sensor Breakout - 20cm, VCNL4040 board
Other useful links
Summary

Created December 1, 2019 © GPL3+

Air Quality Prediction

The system will predict the AQI (Air Quality Index) for the next few days in order to warn people of such situation.

IntermediateWork in progress282

Home: 2nd Place

Machine Learning in the Real World

Things used in this project

Hardware components

SparkFun RedBoard Artemis ATP

SparkFun USB 2.0 Cable A to C - 3 Foot

SparkFun Qwiic Cable Kit

SparkFun Micro OLED Breakout (Qwiic)

SparkFun Environmental Combo Breakout - CCS811/BME280 (Qwiic)

Software apps and online services

Arduino IDE

SparkFun CCS811 library

SparkFun BME280 library

Arduino_TensorFlowLite library

Sparkfun Micro OLED library

Story

Overview

Haze is a problem in my area at this time (during monsoon season). This will post a health hazard to people who breath in non-healthy air. The system that I am going to build, will predict the AQI (Air Quality Index) for the next few days in order to warn people of such situation.

How it works?

The prediction is based on the current sensor data and the prediction model built using TensorFlow. OLED will display only if there are people around the system in order not to waste power.

Setup development environment

There are few tutorials about the SparkFun RedBoard Artemis ATP. My project is based on the following two tutorials:

1. SparkFun Low-Power Machine Learning Examples

2. Artemis ATP TensorFlow Sample

Specifically I followed the instructions of the second tutorial above. Now the development is ready.

Here are some of the screen captures:

Uploading to Artemis ATP board

TensorFlow micro speech output

Add SparkFun Micro OLED Breakout board

Follow this guide to setup the SparkFun Micro OLED Breakout board. Run the demo app.

Qwiic Micro OLED Hookup Guide

Sparkfun Micro OLED breakout board

Add SparkFun Environmental Combo Breakout - CCS811/BME280 board

Follow this guide to setup the SparkFun Environmental Combo Breakout - CCS811/BME280 board. Run the sample app.

CCS811/BME280 (Qwiic) Environmental Combo Breakout Hookup Guide

App output

The Model

I do not have data ready for this project. I am going the Hello World data model from the TensorFlow Lite repo for this project. I modified the main sketch (micro_speech.ino) and add the data files (sine_model_data.h and sine_model_data.cpp) to the project.

Using the temperature as the input and get the prediction value:

// start use sine model
float position = static_cast<float>(temperature) /
static_cast<float>(100.0);
float x_val = position * 6.28;
// Place our calculated x value in the model's input tensor
input2->data.f[0] = x_val;
// Run inference, and report any error
invoke_status = interpreter2->Invoke();
if (invoke_status != kTfLiteOk) {
error_reporter->Report("Invoke failed on x_val: %f\n",
static_cast<double>(x_val));
return;
}
// Read the predicted y value from the model's output tensor
float y_val = output2->data.f[0];
PrintValue(y_val);
// end use sine model

1 / 2

The Code

The files attached at the code section are partial of the whole project. The rest of the files are in the origin micro_speech.ino sketch. You can download it the the TensorFlow Lite GitHub repo.

Add SparkFun Proximity Sensor Breakout - 20cm, VCNL4040 board

Follow this guide to setup the SparkFun Proximity Sensor Breakout - 20cm, VCNL4040 board and run the demo app.

Qwiic Proximity Sensor (VCNL4040) Hookup Guide

I have not completed the implementation of the proximity sensor in this project due to the timing issue. I may complete at a later date.

Summary

This is a partially completed project. A lot of work needed to be done, such as collecting data and build and training a relevant model, adding the proximity sensor and putting up a smooth and user friendly UI.

/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/

#include <Wire.h>  // Include Wire if you're using I2C
#include <SFE_MicroOLED.h>  // Include the SFE_MicroOLED library

//////////////////////////
// MicroOLED Definition //
//////////////////////////
//The library assumes a reset pin is necessary. The Qwiic OLED has RST hard-wired, so pick an arbitrarty IO pin that is not being used
#define PIN_RESET 9  
//The DC_JUMPER is the I2C Address Select jumper. Set to 1 if the jumper is open (Default), or set to 0 if it's closed.
#define DC_JUMPER 1 


//////////////////////////////////
// MicroOLED Object Declaration //
//////////////////////////////////
MicroOLED oled(PIN_RESET, DC_JUMPER);    // I2C declaration

#include <SparkFunBME280.h>
#include <SparkFunCCS811.h>

#define CCS811_ADDR 0x5B //Default I2C Address
//#define CCS811_ADDR 0x5A //Alternate I2C Address

//Global sensor objects
CCS811 myCCS811(CCS811_ADDR);
BME280 myBME280;

// Undefine predefined Arduino max/min defs so they don't conflict with std methods
#if defined(min)
#undef min
#endif

#if defined(max)
#undef max
#endif


#include <TensorFlowLite.h>

#include "main_functions.h"

#include "audio_provider.h"
#include "command_responder.h"
#include "feature_provider.h"
#include "micro_features_micro_model_settings.h"
#include "micro_features_tiny_conv_micro_features_model_data.h"
#include "sine_model_data.h"
#include "recognize_commands.h"
#include "tensorflow/lite/experimental/micro/kernels/micro_ops.h"
#include "tensorflow/lite/experimental/micro/micro_error_reporter.h"
#include "tensorflow/lite/experimental/micro/micro_interpreter.h"
#include "tensorflow/lite/experimental/micro/micro_mutable_op_resolver.h"
#include "tensorflow/lite/schema/schema_generated.h"
#include "tensorflow/lite/version.h"

// Globals, used for compatibility with Arduino-style sketches.
namespace {
tflite::ErrorReporter* error_reporter = nullptr;
const tflite::Model* model = nullptr;
const tflite::Model* model2 = nullptr;
tflite::MicroInterpreter* interpreter = nullptr;
tflite::MicroInterpreter* interpreter2 = nullptr;
TfLiteTensor* model_input = nullptr;
TfLiteTensor* input2 = nullptr;
TfLiteTensor* output2 = nullptr;
FeatureProvider* feature_provider = nullptr;
RecognizeCommands* recognizer = nullptr;
int32_t previous_time = 0;

float temperature = 0.0;

// Create an area of memory to use for input, output, and intermediate arrays.
// The size of this will depend on the model you're using, and may need to be
// determined by experimentation.
constexpr int kTensorArenaSize = 10 * 1024;
uint8_t tensor_arena[kTensorArenaSize];
uint8_t tensor_arena2[kTensorArenaSize];
}  // namespace

// The name of this function is important for Arduino compatibility.
void setup() {
  Wire.begin();
    
  delay(100);
  oled.begin();    // Initialize the OLED
  oled.clear(ALL); // Clear the display's internal memory
  oled.display();  // Display what's in the buffer (splashscreen)
  delay(1000);     // Delay 1000 ms
  oled.clear(PAGE); // Clear the buffer.
  oled.display();

  oled.setFontType(0);
  oled.setCursor(0,0);

  
  // Set up logging. Google style is to avoid globals or statics because of
  // lifetime uncertainty, but since this has a trivial destructor it's okay.
  // NOLINTNEXTLINE(runtime-global-variables)
  static tflite::MicroErrorReporter micro_error_reporter;
  error_reporter = &micro_error_reporter;


  
  //This begins the CCS811 sensor and prints error status of .begin()
  CCS811Core::status returnCode = myCCS811.begin();
  if (returnCode != CCS811Core::SENSOR_SUCCESS)
  {
    error_reporter->Report("Problem with CCS811");
  }
  else
  {
    error_reporter->Report("CCS811 online");
  }
  
  
  //Initialize BME280
  //For I2C, enable the following and disable the SPI section
  myBME280.settings.commInterface = I2C_MODE;
  myBME280.settings.I2CAddress = 0x77;
  myBME280.settings.runMode = 3; //Normal mode
  myBME280.settings.tStandby = 0;
  myBME280.settings.filter = 4;
  myBME280.settings.tempOverSample = 5;
  myBME280.settings.pressOverSample = 5;
  myBME280.settings.humidOverSample = 5;

  //Calling .begin() causes the settings to be loaded
  delay(10);  //Make sure sensor had enough time to turn on. BME280 requires 2ms to start up.
  byte id = myBME280.begin(); //Returns ID of 0x60 if successful
  if (id != 0x60)
  {
    error_reporter->Report("Problem with BME280");
  }
  else
  {
    error_reporter->Report("BME280 online");
  }
  
  
  // Map the model into a usable data structure. This doesn't involve any
  // copying or parsing, it's a very lightweight operation.
  model = tflite::GetModel(g_tiny_conv_micro_features_model_data);
  if (model->version() != TFLITE_SCHEMA_VERSION) {
    error_reporter->Report(
        "Model provided is schema version %d not equal "
        "to supported version %d.",
        model->version(), TFLITE_SCHEMA_VERSION);
    return;
  }

  // Pull in only the operation implementations we need.
  // This relies on a complete list of all the ops needed by this graph.
  // An easier approach is to just use the AllOpsResolver, but this will
  // incur some penalty in code space for op implementations that are not
  // needed by this graph.
  //
  // tflite::ops::micro::AllOpsResolver resolver;
  // NOLINTNEXTLINE(runtime-global-variables)
  static tflite::MicroMutableOpResolver micro_mutable_op_resolver;
  micro_mutable_op_resolver.AddBuiltin(
      tflite::BuiltinOperator_DEPTHWISE_CONV_2D,
      tflite::ops::micro::Register_DEPTHWISE_CONV_2D());
  micro_mutable_op_resolver.AddBuiltin(
      tflite::BuiltinOperator_FULLY_CONNECTED,
      tflite::ops::micro::Register_FULLY_CONNECTED());
  micro_mutable_op_resolver.AddBuiltin(tflite::BuiltinOperator_SOFTMAX,
                                       tflite::ops::micro::Register_SOFTMAX());

  // Build an interpreter to run the model with.
  static tflite::MicroInterpreter static_interpreter(
      model, micro_mutable_op_resolver, tensor_arena, kTensorArenaSize,
      error_reporter);
  interpreter = &static_interpreter;

  // Allocate memory from the tensor_arena for the model's tensors.
  TfLiteStatus allocate_status = interpreter->AllocateTensors();
  if (allocate_status != kTfLiteOk) {
    error_reporter->Report("AllocateTensors() failed");
    return;
  }

  // Get information about the memory area to use for the model's input.
  model_input = interpreter->input(0);
  if ((model_input->dims->size != 4) || (model_input->dims->data[0] != 1) ||
      (model_input->dims->data[1] != kFeatureSliceCount) ||
      (model_input->dims->data[2] != kFeatureSliceSize) ||
      (model_input->type != kTfLiteUInt8)) {
    error_reporter->Report("Bad input tensor parameters in model");
    return;
  }

  // Prepare to access the audio spectrograms from a microphone or other source
  // that will provide the inputs to the neural network.
  // NOLINTNEXTLINE(runtime-global-variables)
  static FeatureProvider static_feature_provider(kFeatureElementCount,
                                                 model_input->data.uint8);
  feature_provider = &static_feature_provider;

  static RecognizeCommands static_recognizer(error_reporter);
  recognizer = &static_recognizer;


// start sine model

  // Map the model into a usable data structure. This doesn't involve any
  // copying or parsing, it's a very lightweight operation.
  model2 = tflite::GetModel(g_sine_model_data);
  if (model2->version() != TFLITE_SCHEMA_VERSION) {
    error_reporter->Report(
                         "Model provided is schema version %d not equal "
                         "to supported version %d.",
                         model2->version(), TFLITE_SCHEMA_VERSION);
    return;
  }
  

//  // This pulls in all the operation implementations we need.
//  // NOLINTNEXTLINE(runtime-global-variables)
//  static tflite::ops::micro::AllOpsResolver resolver;

  static tflite::MicroMutableOpResolver micro_mutable_op_resolver2;
  micro_mutable_op_resolver2.AddBuiltin(
      tflite::BuiltinOperator_DEPTHWISE_CONV_2D,
      tflite::ops::micro::Register_DEPTHWISE_CONV_2D());
  micro_mutable_op_resolver2.AddBuiltin(
      tflite::BuiltinOperator_FULLY_CONNECTED,
      tflite::ops::micro::Register_FULLY_CONNECTED());
  micro_mutable_op_resolver2.AddBuiltin(tflite::BuiltinOperator_SOFTMAX,
                                       tflite::ops::micro::Register_SOFTMAX());
                                       
//  // Build an interpreter to run the model with.
//  static tflite::MicroInterpreter static_interpreter(
//      model, resolver, tensor_arena, kTensorArenaSize, error_reporter);
//  interpreter = &static_interpreter;

  static tflite::MicroInterpreter static_interpreter2(
      model2, micro_mutable_op_resolver2, tensor_arena2, kTensorArenaSize,
      error_reporter);
  interpreter2 = &static_interpreter2;


  // Allocate memory from the tensor_arena for the model's tensors.
  TfLiteStatus allocate_status2 = interpreter2->AllocateTensors();
  if (allocate_status2 != kTfLiteOk) {
    error_reporter->Report("AllocateTensors() failed");
    return;
  }

  // Obtain pointers to the model's input and output tensors.
  input2 = interpreter2->input(0);
  output2 = interpreter2->output(0);
  
// end sine model

  previous_time = 0;
}

// The name of this function is important for Arduino compatibility.
void loop() {
  // Fetch the spectrogram for the current time.
  const int32_t current_time = LatestAudioTimestamp();
  int how_many_new_slices = 0;
  TfLiteStatus feature_status = feature_provider->PopulateFeatureData(
      error_reporter, previous_time, current_time, &how_many_new_slices);
  if (feature_status != kTfLiteOk) {
    error_reporter->Report("Feature generation failed");
    return;
  }
  previous_time = current_time;
  // If no new audio samples have been received since last time, don't bother
  // running the network model.
  if (how_many_new_slices == 0) {
    return;
  }

  // Run the model on the spectrogram input and make sure it succeeds.
  TfLiteStatus invoke_status = interpreter->Invoke();
  if (invoke_status != kTfLiteOk) {
    error_reporter->Report("Invoke failed");
    return;
  }

  // Obtain a pointer to the output tensor
  TfLiteTensor* output = interpreter->output(0);
  // Determine whether a command was recognized based on the output of inference
  const char* found_command = nullptr;
  uint8_t score = 0;
  bool is_new_command = false;
  TfLiteStatus process_status = recognizer->ProcessLatestResults(
      output, current_time, &found_command, &score, &is_new_command);
  if (process_status != kTfLiteOk) {
    error_reporter->Report("RecognizeCommands::ProcessLatestResults() failed");
    return;
  }
  // Do something based on the recognized command. The default implementation
  // just prints to the error console, but you should replace this with your
  // own function for a real application.
  RespondToCommand(error_reporter, PrintCommand, current_time, found_command, score,
                   is_new_command);

  //Check to see if data is available
  if (is_new_command && myCCS811.dataAvailable())
  {
    //Calling this function updates the global tVOC and eCO2 variables
    myCCS811.readAlgorithmResults();
    //printData fetches the values of tVOC and eCO2
    PrintData();
  }

// start use sine model

  float position = static_cast<float>(temperature) /
                   static_cast<float>(100.0);
  float x_val = position * 6.28;

  // Place our calculated x value in the model's input tensor
  input2->data.f[0] = x_val;

  // Run inference, and report any error
  invoke_status = interpreter2->Invoke();
  if (invoke_status != kTfLiteOk) {
    error_reporter->Report("Invoke failed on x_val: %f\n",
                         static_cast<double>(x_val));
    return;
  }

  // Read the predicted y value from the model's output tensor
  float y_val = output2->data.f[0];

  PrintValue(y_val);
  
// end use sine model  
}


void PrintCommand(String command) {
  oled.clear(PAGE);
  oled.print(command);
  oled.display();
  delay(1500);
  oled.clear(PAGE);
  oled.display();
  oled.setCursor(0,0);
}

void PrintData() {
  oled.clear(PAGE);
  oled.print("CO2  ");
  oled.print(myCCS811.getCO2());
  oled.print("\nTVOC ");
  oled.print(myCCS811.getTVOC());
  oled.print("\nTEMP ");
  temperature = myBME280.readTempC();
  oled.print(temperature, 1); oled.print("C");
  oled.print("PRES ");
  oled.print(myBME280.readFloatPressure()/1000, 2); oled.print("KPa");
  oled.print("\nHUMI ");
  oled.print(myBME280.readFloatHumidity(), 0); oled.print("%");
  oled.display();
  delay(5000);
  oled.clear(PAGE);
  oled.display();
  oled.setCursor(0,0);  
}

void PrintValue(float value) { 
  oled.clear(PAGE);
  oled.print("Prediction ");
  oled.print(value);
  oled.display();
  delay(10000);
  oled.clear(PAGE);
  oled.display();
  oled.setCursor(0,0);
}

/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/

// This is a standard TensorFlow Lite model file that has been converted into a
// C data array, so it can be easily compiled into a binary for devices that
// don't have a file system. It was created using the command:
// xxd -i sine_model.tflite > sine_model_data.cc

#ifndef TENSORFLOW_LITE_MICRO_EXAMPLES_HELLO_WORLD_SINE_MODEL_DATA_H_
#define TENSORFLOW_LITE_MICRO_EXAMPLES_HELLO_WORLD_SINE_MODEL_DATA_H_

extern const unsigned char g_sine_model_data[];
extern const int g_sine_model_data_len;

#endif  // TENSORFLOW_LITE_MICRO_EXAMPLES_HELLO_WORLD_SINE_MODEL_DATA_H_

/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/

// Automatically created from a TensorFlow Lite flatbuffer using the command:
// xxd -i sine_model.tflite > sine_model_data.cc
// See the README for a full description of the creation process.

#include "sine_model_data.h"

// We need to keep the data array aligned on some architectures.
#ifdef __has_attribute
#define HAVE_ATTRIBUTE(x) __has_attribute(x)
#else
#define HAVE_ATTRIBUTE(x) 0
#endif
#if HAVE_ATTRIBUTE(aligned) || (defined(__GNUC__) && !defined(__clang__))
#define DATA_ALIGN_ATTRIBUTE __attribute__((aligned(4)))
#else
#define DATA_ALIGN_ATTRIBUTE
#endif

const unsigned char g_sine_model_data[] DATA_ALIGN_ATTRIBUTE = {
    0x18, 0x00, 0x00, 0x00, 0x54, 0x46, 0x4c, 0x33, 0x00, 0x00, 0x0e, 0x00,
    0x18, 0x00, 0x04, 0x00, 0x08, 0x00, 0x0c, 0x00, 0x10, 0x00, 0x14, 0x00,
    0x0e, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, 0x10, 0x0a, 0x00, 0x00,
    0xb8, 0x05, 0x00, 0x00, 0xa0, 0x05, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00,
    0x0b, 0x00, 0x00, 0x00, 0x90, 0x05, 0x00, 0x00, 0x7c, 0x05, 0x00, 0x00,
    0x24, 0x05, 0x00, 0x00, 0xd4, 0x04, 0x00, 0x00, 0xc4, 0x00, 0x00, 0x00,
    0x74, 0x00, 0x00, 0x00, 0x24, 0x00, 0x00, 0x00, 0x1c, 0x00, 0x00, 0x00,
    0x14, 0x00, 0x00, 0x00, 0x0c, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00,
    0x54, 0xf6, 0xff, 0xff, 0x58, 0xf6, 0xff, 0xff, 0x5c, 0xf6, 0xff, 0xff,
    0x60, 0xf6, 0xff, 0xff, 0xc2, 0xfa, 0xff, 0xff, 0x04, 0x00, 0x00, 0x00,
    0x40, 0x00, 0x00, 0x00, 0x7c, 0x19, 0xa7, 0x3e, 0x99, 0x81, 0xb9, 0x3e,
    0x56, 0x8b, 0x9f, 0x3e, 0x88, 0xd8, 0x12, 0xbf, 0x74, 0x10, 0x56, 0x3e,
    0xfe, 0xc6, 0xdf, 0xbe, 0xf2, 0x10, 0x5a, 0xbe, 0xf0, 0xe2, 0x0a, 0xbe,
    0x10, 0x5a, 0x98, 0xbe, 0xb9, 0x36, 0xce, 0x3d, 0x8f, 0x7f, 0x87, 0x3e,
    0x2c, 0xb1, 0xfd, 0xbd, 0xe6, 0xa6, 0x8a, 0xbe, 0xa5, 0x3e, 0xda, 0x3e,
    0x50, 0x34, 0xed, 0xbd, 0x90, 0x91, 0x69, 0xbe, 0x0e, 0xfb, 0xff, 0xff,
    0x04, 0x00, 0x00, 0x00, 0x40, 0x00, 0x00, 0x00, 0x67, 0x41, 0x48, 0xbf,
    0x24, 0xcd, 0xa0, 0xbe, 0xb7, 0x92, 0x0c, 0xbf, 0x00, 0x00, 0x00, 0x00,
    0x98, 0xfe, 0x3c, 0x3f, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
    0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4a, 0x17, 0x9a, 0xbe,
    0x41, 0xcb, 0xb6, 0xbe, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
    0x13, 0xd6, 0x1e, 0x3e, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
    0x5a, 0xfb, 0xff, 0xff, 0x04, 0x00, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00,
    0x4b, 0x98, 0xdd, 0xbd, 0x40, 0x6b, 0xcb, 0xbe, 0x36, 0x0c, 0xd4, 0x3c,
    0xbd, 0x44, 0xb5, 0x3e, 0x95, 0x70, 0xe3, 0x3e, 0xe7, 0xac, 0x86, 0x3e,
    0x00, 0xc4, 0x4e, 0x3d, 0x7e, 0xa6, 0x1d, 0x3e, 0xbd, 0x87, 0xbb, 0x3e,
    0xb4, 0xb8, 0x09, 0xbf, 0xa1, 0x1f, 0xf8, 0xbe, 0x8d, 0x90, 0xdd, 0x3e,
    0xde, 0xfa, 0x6f, 0xbe, 0xb2, 0x75, 0xe4, 0x3d, 0x6e, 0xfe, 0x36, 0x3e,
    0x20, 0x18, 0xc2, 0xbe, 0x39, 0xc7, 0xfb, 0xbe, 0xfe, 0xa4, 0x30, 0xbe,
    0xf7, 0x91, 0xde, 0xbe, 0xde, 0xab, 0x24, 0x3e, 0xfb, 0xbb, 0xce, 0x3e,
    0xeb, 0x23, 0x80, 0xbe, 0x7b, 0x58, 0x73, 0xbe, 0x9a, 0x2e, 0x03, 0x3e,
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const int g_sine_model_data_len = 2640;

Credits

vincent wong

80 projects • 203 followers

mok mun fong

2 projects • 2 followers

Comments

Awards

Home: 2nd Place

Machine Learning in the Real World

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Air Quality Prediction

Air Quality Prediction

Things used in this project

Hardware components

Software apps and online services

Story

Overview

How it works?

Setup development environment

Add SparkFun Micro OLED Breakout board

Add SparkFun Environmental Combo Breakout - CCS811/BME280 board

The Model

The Code

Add SparkFun Proximity Sensor Breakout - 20cm, VCNL4040 board

Other useful links

Summary

Code

micro_speech_1.ino

sine_model_data.h

sine_model_data.cpp

Credits

vincent wong

mok mun fong

Comments

Awards

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