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OverviewHaze 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 environmentThere 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 boardFollow this guide to setup the SparkFun Micro OLED Breakout board. Run the demo app.
.
Sparkfun Micro OLED breakout board
.
Add SparkFun Environmental Combo Breakout - CCS811/BME280 boardFollow 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 ModelI 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 CodeThe 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 boardFollow 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.
.
Other useful linksHookup Guide for the SparkFun RedBoard Artemis ATP
Artemis Development with Arduino
Using SparkFun Edge Board with Ambiq Apollo3 SDK
.
SummaryThis 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 = µ_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,
0x10, 0x42, 0xa9, 0xbc, 0x10, 0x12, 0x64, 0xbd, 0xe3, 0x8d, 0x0c, 0x3d,
0x9e, 0x48, 0x97, 0xbe, 0x34, 0x51, 0xd4, 0xbe, 0x02, 0x3b, 0x0d, 0x3e,
0x62, 0x67, 0x89, 0xbe, 0x74, 0xdf, 0xa2, 0x3d, 0xf3, 0x25, 0xb3, 0xbe,
0xef, 0x34, 0x7b, 0x3d, 0x61, 0x70, 0xe3, 0x3d, 0xba, 0x76, 0xc0, 0xbe,
0x7d, 0xe9, 0xa7, 0x3e, 0xc3, 0xab, 0xd0, 0xbe, 0xcf, 0x7c, 0xdb, 0xbe,
0x70, 0x27, 0x9a, 0xbe, 0x98, 0xf5, 0x3c, 0xbd, 0xff, 0x4b, 0x4b, 0x3e,
0x7e, 0xa0, 0xf8, 0xbd, 0xd4, 0x6e, 0x86, 0x3d, 0x00, 0x4a, 0x07, 0x3a,
0x4c, 0x24, 0x61, 0xbe, 0x54, 0x68, 0xf7, 0xbd, 0x02, 0x3f, 0x77, 0xbe,
0x23, 0x79, 0xb3, 0x3e, 0x1c, 0x83, 0xad, 0xbd, 0xc8, 0x92, 0x8d, 0x3e,
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0x0c, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x38, 0xff, 0xff, 0xff,
0x34, 0x00, 0x00, 0x00, 0x73, 0x65, 0x71, 0x75, 0x65, 0x6e, 0x74, 0x69,
0x61, 0x6c, 0x5f, 0x31, 0x2f, 0x64, 0x65, 0x6e, 0x73, 0x65, 0x5f, 0x32,
0x2f, 0x4d, 0x61, 0x74, 0x4d, 0x75, 0x6c, 0x2f, 0x52, 0x65, 0x61, 0x64,
0x56, 0x61, 0x72, 0x69, 0x61, 0x62, 0x6c, 0x65, 0x4f, 0x70, 0x2f, 0x74,
0x72, 0x61, 0x6e, 0x73, 0x70, 0x6f, 0x73, 0x65, 0x00, 0x00, 0x00, 0x00,
0x02, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00,
0xa6, 0xff, 0xff, 0xff, 0x48, 0x00, 0x00, 0x00, 0x09, 0x00, 0x00, 0x00,
0x2c, 0x00, 0x00, 0x00, 0x0c, 0x00, 0x00, 0x00, 0x08, 0x00, 0x0c, 0x00,
0x04, 0x00, 0x08, 0x00, 0x08, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00,
0x04, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x7f, 0x43,
0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0d, 0x00, 0x00, 0x00,
0x64, 0x65, 0x6e, 0x73, 0x65, 0x5f, 0x32, 0x5f, 0x69, 0x6e, 0x70, 0x75,
0x74, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00,
0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0e, 0x00, 0x14, 0x00, 0x04, 0x00,
0x00, 0x00, 0x08, 0x00, 0x0c, 0x00, 0x10, 0x00, 0x0e, 0x00, 0x00, 0x00,
0x28, 0x00, 0x00, 0x00, 0x07, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00,
0x08, 0x00, 0x00, 0x00, 0x04, 0x00, 0x04, 0x00, 0x04, 0x00, 0x00, 0x00,
0x08, 0x00, 0x00, 0x00, 0x49, 0x64, 0x65, 0x6e, 0x74, 0x69, 0x74, 0x79,
0x00, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00,
0x01, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00,
0x00, 0x00, 0x0a, 0x00, 0x0c, 0x00, 0x07, 0x00, 0x00, 0x00, 0x08, 0x00,
0x0a, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x09, 0x03, 0x00, 0x00, 0x00};
const int g_sine_model_data_len = 2640;
Comments