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- Motivation
Human behavior is a rich data source that can be acquired in all kinds of daily scenes. It will be quite meaningful for agents to figure out what's going on with a specific person and further contribute to his or her next move. However, to deal with some complex data collected by the sensors, hand-crafted algorithms based on human expertise are required, which is not extendable between diverse application scenarios. Therefore, a machine learning algorithm is a good solution which can be easily applied under different scenarios, enabling the model to learn the relationship between complex data without high domain knowledge. To make machine learning applicable in resource-constrained platforms like smartphone, smartwatch or other daily-use devices, compression techniques like pruning and quantization and customized framework are necessary. That's exactly what TFLite does. In this project, we will build a demo, i.e. apply a machine learning algorithm on Arduino Nano 33 to build a magic wand based on TFLite framework, to show the potential of embedded machine learning in automated industry.
- Objective
3 kinds of gestures
- Design Structure
Design Structure
The 9 axis inertial sensor will send the current acceleration of the board to the handler that collects data for the Machine Learning Model. And the model has been already pre-trained offline, so it can predict the gesture directly by sending the extracted feature to the Gesture Predictor. Finally, the Handler for visualization could send the corresponding gesture to the Displayer, and we can see it from the terminal in Arduino IDE.
- Deployment
To run this project, we could follow the instructions below.
1. Add corresponding libraries
After installing Arduino IDE from Arduino official website, we click Tool -> Manage Libraries and search "Arduino_TensorFlowLite" on the pop-up. It will show the following result.
search "Arduino_TensorFlowLite"
After click "install", it will install this library for you automatically.
Then, repeat the above process to install "Arduino_LSM9DS1" to handle the 9 axis inertial sensor in the Nano 33BLE. Please make sure the one you installed is provided by Arduino as shown by the following figure.
search "Arduino_LSM9DS1"
2. Modify some details in the added libraries
To use the LSM9DS1 sensor in our project, some modification to the source file is needed.
For Windows user, the source file could be found in "C:\Users\username\Documents\Arduino\libraries\Arduino_LSM9DS1", for Linux/Unix user, the source file could be found in "/home/username/Arduino_LSM9DS1/".
Then open the "src/LSM9DS1.cpp" and find the "LSM9DS1Class::begin()", then insert the following code right before the "return".
// Enable FIFO (see docs https://www.st.com/resource/en/datasheet/DM00103319.pdf)
writeRegister(LSM9DS1_ADDRESS, 0x23, 0x02);
// Set continuous mode
writeRegister(LSM9DS1_ADDRESS, 0x2E, 0xC0);Also, please find the "LSM9DS1Class::accelerationAvailable()" and do some replacement as shown by the following code.
int LSM9DS1Class::accelerationAvailable()
{
/*
if (readRegister(LSM9DS1_ADDRESS, LSM9DS1_STATUS_REG) & 0x01) {
return 1;
}
*/
// Read FIFO_SRC. If any of the rightmost 8 bits have a value, there is data
if (readRegister(LSM9DS1_ADDRESS, 0x2F) & 63) {
return 1;
}
return 0;
}Please save it after finishing the above modification.
3. Load code for Magic Wand
Then we can load the official example and play with it.
In the Arduino IDE, click File-> Examples -> TensorFlowLite -> magic_wand, then you can see the example c++ code for this application and no further modification is needed.
Just go on and select the board we are using by click Tools -> Board -> Arduino Nano 33 BLE. Then click Tools -> Port -> COM* or dev/ttyACM* (varying on different computers) to set up the port for flashing code and serial communication.
Then press the "upload" button in the Arduino IDE and click Tools -> Serial Monitor after the uploading to visualize the gesture prediction as shown by the following video demo.
- Video Demo
Here's the video demo to visualize the utilization of magic wand.
/* 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.
==============================================================================*/
#ifndef TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MAGIC_WAND_ACCELEROMETER_HANDLER_H_
#define TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MAGIC_WAND_ACCELEROMETER_HANDLER_H_
#define kChannelNumber 3
#include "tensorflow/lite/c/c_api_internal.h"
#include "tensorflow/lite/experimental/micro/micro_error_reporter.h"
extern int begin_index;
extern TfLiteStatus SetupAccelerometer(tflite::ErrorReporter* error_reporter);
extern bool ReadAccelerometer(tflite::ErrorReporter* error_reporter,
float* input, int length, bool reset_buffer);
#endif // TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MAGIC_WAND_ACCELEROMETER_HANDLER_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.
==============================================================================*/
// 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 magic_wand_model.tflite > magic_wand_model_data.cc
#ifndef TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MAGIC_WAND_MAGIC_WAND_MODEL_DATA_H_
#define TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MAGIC_WAND_MAGIC_WAND_MODEL_DATA_H_
extern const unsigned char g_magic_wand_model_data[];
extern const int g_magic_wand_model_data_len;
#endif // TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MAGIC_WAND_MAGIC_WAND_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.
==============================================================================*/
#ifndef TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MAGIC_WAND_MAIN_FUNCTIONS_H_
#define TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MAGIC_WAND_MAIN_FUNCTIONS_H_
// Initializes all data needed for the example. The name is important, and needs
// to be setup() for Arduino compatibility.
void setup();
// Runs one iteration of data gathering and inference. This should be called
// repeatedly from the application code. The name needs to be loop() for Arduino
// compatibility.
void loop();
#endif // TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MAGIC_WAND_MAIN_FUNCTIONS_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.
==============================================================================*/
#ifndef TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MAGIC_WAND_OUTPUT_HANDLER_H_
#define TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MAGIC_WAND_OUTPUT_HANDLER_H_
#include "tensorflow/lite/c/c_api_internal.h"
#include "tensorflow/lite/experimental/micro/micro_error_reporter.h"
void HandleOutput(tflite::ErrorReporter* error_reporter, int kind);
#endif // TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MAGIC_WAND_OUTPUT_HANDLER_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.
==============================================================================*/
#include "accelerometer_handler.h"
#include <Arduino.h>
#include <Arduino_LSM9DS1.h>
#include "constants.h"
// A buffer holding the last 200 sets of 3-channel values
float save_data[600] = {0.0};
// Most recent position in the save_data buffer
int begin_index = 0;
// True if there is not yet enough data to run inference
bool pending_initial_data = true;
// How often we should save a measurement during downsampling
int sample_every_n;
// The number of measurements since we last saved one
int sample_skip_counter = 1;
TfLiteStatus SetupAccelerometer(tflite::ErrorReporter* error_reporter) {
// Wait until we know the serial port is ready
while (!Serial) {
}
// Switch on the IMU
if (!IMU.begin()) {
error_reporter->Report("Failed to initialize IMU");
return kTfLiteError;
}
// Determine how many measurements to keep in order to
// meet kTargetHz
float sample_rate = IMU.accelerationSampleRate();
sample_every_n = static_cast<int>(roundf(sample_rate / kTargetHz));
error_reporter->Report("Magic starts!");
return kTfLiteOk;
}
bool ReadAccelerometer(tflite::ErrorReporter* error_reporter, float* input,
int length, bool reset_buffer) {
// Clear the buffer if required, e.g. after a successful prediction
if (reset_buffer) {
memset(save_data, 0, 600 * sizeof(float));
begin_index = 0;
pending_initial_data = true;
}
// Keep track of whether we stored any new data
bool new_data = false;
// Loop through new samples and add to buffer
while (IMU.accelerationAvailable()) {
float x, y, z;
// Read each sample, removing it from the device's FIFO buffer
if (!IMU.readAcceleration(x, y, z)) {
error_reporter->Report("Failed to read data");
break;
}
// Throw away this sample unless it's the nth
if (sample_skip_counter != sample_every_n) {
sample_skip_counter += 1;
continue;
}
// Write samples to our buffer, converting to milli-Gs
// and flipping y and x order for compatibility with
// model (sensor orientation is different on Arduino
// Nano BLE Sense compared with SparkFun Edge)
save_data[begin_index++] = y * 1000;
save_data[begin_index++] = x * 1000;
save_data[begin_index++] = z * 1000;
// Since we took a sample, reset the skip counter
sample_skip_counter = 1;
// If we reached the end of the circle buffer, reset
if (begin_index >= 600) {
begin_index = 0;
}
new_data = true;
}
// Skip this round if data is not ready yet
if (!new_data) {
return false;
}
// Check if we are ready for prediction or still pending more initial data
if (pending_initial_data && begin_index >= 200) {
pending_initial_data = false;
}
// Return if we don't have enough data
if (pending_initial_data) {
return false;
}
// Copy the requested number of bytes to the provided input tensor
for (int i = 0; i < length; ++i) {
int ring_array_index = begin_index + i - length;
if (ring_array_index < 0) {
ring_array_index += 600;
}
input[i] = save_data[ring_array_index];
}
return true;
}
/* 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.
==============================================================================*/
#include "constants.h"
// The number of expected consecutive inferences for each gesture type.
// Established with the Arduino Nano 33 BLE Sense.
const int kConsecutiveInferenceThresholds[3] = {8, 5, 4};
/* 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.
==============================================================================*/
#include "main_functions.h"
// Arduino automatically calls the setup() and loop() functions in a sketch, so
// where other systems need their own main routine in this file, it can be left
// empty.
/* 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.
==============================================================================*/
#include "output_handler.h"
#include "Arduino.h"
void HandleOutput(tflite::ErrorReporter* error_reporter, int kind) {
// The first time this method runs, set up our LED
static bool is_initialized = false;
if (!is_initialized) {
pinMode(LED_BUILTIN, OUTPUT);
is_initialized = true;
}
// Toggle the LED every time an inference is performed
static int count = 0;
++count;
if (count & 1) {
digitalWrite(LED_BUILTIN, HIGH);
} else {
digitalWrite(LED_BUILTIN, LOW);
}
// Print some ASCII art for each gesture
if (kind == 0) {
error_reporter->Report(
"WING:\n\r* * *\n\r * * * "
"*\n\r * * * *\n\r * * * *\n\r * * "
"* *\n\r * *\n\r");
} else if (kind == 1) {
error_reporter->Report(
"RING:\n\r *\n\r * *\n\r * *\n\r "
" * *\n\r * *\n\r * *\n\r "
" *\n\r");
} else if (kind == 2) {
error_reporter->Report(
"SLOPE:\n\r *\n\r *\n\r *\n\r *\n\r "
"*\n\r *\n\r *\n\r * * * * * * * *\n\r");
}
}
/* 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.
==============================================================================*/
#ifndef TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MAGIC_WAND_CONSTANTS_H_
#define TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MAGIC_WAND_CONSTANTS_H_
// The expected accelerometer data sample frequency
const float kTargetHz = 25;
// The number of expected consecutive inferences for each gesture type
extern const int kConsecutiveInferenceThresholds[3];
#endif // TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MAGIC_WAND_CONSTANTS_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.
==============================================================================*/
#include "gesture_predictor.h"
#include "constants.h"
// How many times the most recent gesture has been matched in a row
int continuous_count = 0;
// The result of the last prediction
int last_predict = -1;
// Return the result of the last prediction
// 0: wing("W"), 1: ring("O"), 2: slope("angle"), 3: unknown
int PredictGesture(float* output) {
// Find whichever output has a probability > 0.8 (they sum to 1)
int this_predict = -1;
for (int i = 0; i < 3; i++) {
if (output[i] > 0.8) this_predict = i;
}
// No gesture was detected above the threshold
if (this_predict == -1) {
continuous_count = 0;
last_predict = 3;
return 3;
}
if (last_predict == this_predict) {
continuous_count += 1;
} else {
continuous_count = 0;
}
last_predict = this_predict;
// If we haven't yet had enough consecutive matches for this gesture,
// report a negative result
if (continuous_count < kConsecutiveInferenceThresholds[this_predict]) {
return 3;
}
// Otherwise, we've seen a positive result, so clear all our variables
// and report it
continuous_count = 0;
last_predict = -1;
return this_predict;
}
/* 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.
==============================================================================*/
#ifndef TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MAGIC_WAND_GESTURE_PREDICTOR_H_
#define TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MAGIC_WAND_GESTURE_PREDICTOR_H_
extern int PredictGesture(float* output);
#endif // TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MAGIC_WAND_GESTURE_PREDICTOR_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 magic_wand_model.tflite > magic_wand_model_data.cc
// See the README for a full description of the creation process.
#include "magic_wand_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_magic_wand_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, 0x18, 0x4c, 0x00, 0x00,
0x0c, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x20, 0x00, 0x00, 0x00,
0x01, 0x00, 0x00, 0x00, 0x20, 0x44, 0x00, 0x00, 0x0f, 0x00, 0x00, 0x00,
0x54, 0x4f, 0x43, 0x4f, 0x20, 0x43, 0x6f, 0x6e, 0x76, 0x65, 0x72, 0x74,
0x65, 0x64, 0x2e, 0x00, 0x11, 0x00, 0x00, 0x00, 0xf4, 0x43, 0x00, 0x00,
0xa4, 0x43, 0x00, 0x00, 0x84, 0x43, 0x00, 0x00, 0x34, 0x43, 0x00, 0x00,
0x2c, 0x43, 0x00, 0x00, 0x1c, 0x42, 0x00, 0x00, 0x14, 0x42, 0x00, 0x00,
0x04, 0x0a, 0x00, 0x00, 0xd4, 0x09, 0x00, 0x00, 0xc4, 0x01, 0x00, 0x00,
0xbc, 0x01, 0x00, 0x00, 0xb4, 0x01, 0x00, 0x00, 0xac, 0x01, 0x00, 0x00,
0x1c, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x0c, 0x00, 0x00, 0x00,
0x04, 0x00, 0x00, 0x00, 0x24, 0xb6, 0xff, 0xff, 0x28, 0xb6, 0xff, 0xff,
0x2c, 0xb6, 0xff, 0xff, 0x76, 0xb6, 0xff, 0xff, 0x04, 0x00, 0x00, 0x00,
0x80, 0x01, 0x00, 0x00, 0x13, 0x00, 0x8a, 0xbe, 0x50, 0x7c, 0x49, 0x3e,
0xb4, 0x06, 0x5a, 0xbc, 0xa6, 0xf2, 0xa8, 0xbd, 0x73, 0x50, 0x62, 0x3c,
0xdd, 0x0a, 0x4a, 0xbe, 0xe2, 0x77, 0x9c, 0x3e, 0x8b, 0xe0, 0xc7, 0xbd,
0xb2, 0xfd, 0xf5, 0x3d, 0x13, 0xb1, 0xb2, 0xbe, 0xcd, 0x78, 0x5a, 0xbd,
0xfb, 0x15, 0x4e, 0xbc, 0x58, 0x7b, 0x3b, 0x3e, 0x14, 0x4b, 0x22, 0xbc,
0x7e, 0x44, 0xd2, 0x3d, 0xdc, 0xda, 0x72, 0x3c, 0x1a, 0x87, 0xd9, 0x3d,
0x3e, 0xdc, 0x13, 0x3d, 0x01, 0x1e, 0x75, 0xbe, 0x3d, 0x4f, 0x6a, 0xbd,
0xa6, 0x52, 0x54, 0xbe, 0xc7, 0x7f, 0x5f, 0xbe, 0x97, 0x5f, 0x35, 0xbc,
0xc5, 0x84, 0x5b, 0xbe, 0x7c, 0xd5, 0x6f, 0xbd, 0x90, 0x9b, 0x30, 0xbd,
0x52, 0x86, 0xec, 0xbc, 0xc0, 0x4e, 0x0b, 0xbf, 0xfc, 0x3d, 0xec, 0xbd,
0x92, 0x71, 0x26, 0x3e, 0x34, 0x26, 0x33, 0x3d, 0x06, 0x68, 0xfc, 0xbd,
0x54, 0x5f, 0x2f, 0xbd, 0xa2, 0xce, 0xdd, 0x3d, 0x83, 0x6a, 0x76, 0xbc,
0x64, 0xba, 0x95, 0xbd, 0x44, 0x69, 0x09, 0x3e, 0xea, 0x7b, 0x08, 0x3e,
0xec, 0x13, 0x9f, 0xbd, 0x80, 0x2a, 0x04, 0xbe, 0x64, 0xf5, 0x84, 0x3e,
0x31, 0xf8, 0xb4, 0xbd, 0xfa, 0x18, 0xb3, 0xbd, 0x4b, 0x3d, 0xf9, 0xbc,
0xee, 0x0e, 0x8f, 0xbd, 0x3b, 0x21, 0x39, 0xbc, 0x35, 0xa0, 0xbb, 0xbc,
0xd5, 0x5f, 0xbe, 0xbd, 0x9e, 0xc4, 0x0b, 0x3d, 0x4a, 0x8d, 0x82, 0xbe,
0x01, 0xfb, 0x19, 0xbd, 0xb0, 0x51, 0xae, 0x3c, 0xb5, 0xd8, 0x68, 0xbe,
0x97, 0x45, 0x73, 0x3d, 0xc7, 0x33, 0x2a, 0x3e, 0x9f, 0x82, 0x09, 0x3e,
0x32, 0x36, 0xba, 0xbd, 0x93, 0x0d, 0x7e, 0xbb, 0xc2, 0x5f, 0xa6, 0xbd,
0x13, 0x20, 0x55, 0xbe, 0xbf, 0x03, 0x08, 0xbe, 0xeb, 0xe0, 0xa9, 0xbd,
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