UBISS2024: Difference between revisions
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== Task 3: ML on Arduino Nano Connect RP2040 == | == Task 3: ML on Arduino Nano Connect RP2040 == | ||
We will use https://github.com/eloquentarduino/everywhereml to detect the same gestures as in 2.2. For this, install everywhereml: | |||
<syntaxhighlight lang="Bash"> | |||
pip3 install -U everywhereml | |||
</syntaxhighlight> | |||
== Task 4: connect both boards to WIFI == | == Task 4: connect both boards to WIFI == |
Revision as of 09:17, 10 June 2024
Link Page
https://www.sketching-with-hardware.org/wiki/UBISS2024-Links
Tasks
Project 0: connect a Arduino Nano ESP32 board
- Install the basic software https://labs.arduino.cc/en/labs/micropython
- connect the board via USB
- Make the orange LED (pin 6) blink using micro python https://docs.arduino.cc/micropython/basics/digital-analog-pins/
- Connect an external RGB LED (pin 2, 3, 4), https://www.sketching-with-hardware.org/wiki/RGB_LED
- Control the external RGB LED (on, off, mix color, brightness)
solution Project 0: LED Blinking
# Blinky example
import time
from machine import Pin
# This is the only LED pin available on the Nano RP2040,
# other than the RGB LED connected to Nano WiFi module.
led = Pin(6, Pin.OUT)
while (True):
led.on()
time.sleep_ms(250)
led.off()
time.sleep_ms(200)
solution Project 0: Control external RGB
# RGB example
import time
from machine import Pin
# RGB LED connected to Nano WiFi module.
ledG = Pin(2, Pin.OUT)
ledR = Pin(3, Pin.OUT)
ledB = Pin(4, Pin.OUT)
print("start")
while (True):
print("*")
ledG.on()
ledR.off()
ledB.off()
time.sleep_ms(250)
ledG.off()
ledR.on()
ledB.off()
time.sleep_ms(250)
ledG.off()
ledR.off()
ledB.on()
time.sleep_ms(250)
Project 1: read Acceleration from Arduino Nano ESP32 board
- read data from the accelerometer and the gyro and print them (Arduino IDE) https://docs.arduino.cc/micropython/basics/board-examples/
- extend you program to write the data from the accelerometers to a file, https://www.sketching-with-hardware.org/wiki/FileIO
- transfer the file to your computer
- optional: add the photo resistors to your board, read their values, and write them to the file, too, https://www.sketching-with-hardware.org/wiki/LDR
solution Project 1: Read Accelerometer and Gyro
import time
from lsm6dsox import LSM6DSOX
from machine import Pin, I2C
lsm = LSM6DSOX(I2C(0, scl=Pin(13), sda=Pin(12)))
while (True):
accel_data = lsm.accel()
print('Accelerometer: x:{:>8.3f} y:{:>8.3f} z:{:>8.3f}'.format(*accel_data))
gyro_data = lsm.gyro()
print('Gyroscope: x:{:>8.3f} y:{:>8.3f} z:{:>8.3f}'.format(*gyro_data))
print("")
time.sleep_ms(100)
solution Project 2: Read analog values - Code Example Arduino Nano Connect RP2040
A0 is the analog input with 16 bit resolution. It reads the analog value every second and print it to the console-
#Example usage for Arduino Nano
from machine import Pin, ADC
from time import sleep
analogPin = ADC(Pin(26))
while True:
analogVal16 = analogPin.read_u16()
print(analogVal16)
sleep(1)
Project 2: Jupyter Notebook
- connect the board
- install the Juypter Notebook, https://www.sketching-with-hardware.org/wiki/Jupyter
- read the accelerometer and the gyro and show it in the notebook
Task 2.1: is it moved?
- read acceleration and gyro
- calculate the differences between values
- show an ouput when it is move
- create a file on the device that logs, when it is moved
Task 2.2: it was turned upside down?
- read acceleration and gyro
- make a rule based "AI" that records
- it was put upside down
- it was turned 360
- it was moved "quickly"
Task 3: ML on Arduino Nano Connect RP2040
We will use https://github.com/eloquentarduino/everywhereml to detect the same gestures as in 2.2. For this, install everywhereml:
pip3 install -U everywhereml
Task 4: connect both boards to WIFI
- connect both boards to WIFI using Tutorial_Network
- use the Arduino Nano ESP32 as output (showing a color)
- use the Arduino Nano Connect RP2040 as input (recognize with rules 3 gestures)
Links
See the full list of links: UBISS2024-Links
Local Links
https://ubicomp.net/sw/db1/var2db.php? http://localhost:8888/notebooks/ArduinoNanoRP2040_v01.ipynb http://localhost:8888/doc/tree/create-ML-model01.ipynb
Reading
Required Reading before the course
- Albrecht Schmidt (2020) Interactive Human Centered Artificial Intelligence: A Definition and Research Challenges. In Proceedings of the International Conference on Advanced Visual Interfaces (AVI '20). Association for Computing Machinery, New York, NY, USA, Article 3, 1–4. https://doi.org/10.1145/3399715.3400873 https://uni.ubicomp.net/as/iHCAI2020.pdf (4p)
- Albrecht Schmidt and Kristof van Laerhoven (2021) How to build smart appliances? In IEEE Personal Communications, vol. 8, no. 4, pp. 66-71, Aug. 2001, https://doi.org/10.1109/98.944006 https://www.eti.uni-siegen.de/ubicomp/papers/sl_ieeepc2001.pdf (6p)
- Albrecht Schmidt (2017) Understanding and researching through making: a plea for functional prototypes. interactions 24.3, 78-81. https://doi.org/10.1145/3058498 https://www.sketching-with-hardware.org/files/functional3058498.pdf (4p)
- Huy Viet Le, Sven Mayer, and Niels Henze (2020) Deep learning for human-computer interaction. interactions 28, 1 (January - February 2021), 78–82. https://doi.org/10.1145/3436958 https://sven-mayer.com/wp-content/uploads/2021/01/huy2021deep.pdf (5p)
- Huy Viet Le, Sven Mayer, Max Weiß, Jonas Vogelsang, Henrike Weingärtner, and Niels Henze (2020) Shortcut Gestures for Mobile Text Editing on Fully Touch Sensitive Smartphones. In: ACM Trans. Comput.-Hum. Interact., vol. 27, no. 5, pp. 38. https://sven-mayer.com/wp-content/uploads/2020/09/le2020shortcuts.pdf (38p)
- Judith Hurwitz, and Daniel Kirsch (2018) Machine learning for dummies. IBM Limited Edition 75, 9780429196645-6. https://www.ibm.com/downloads/cas/GB8ZMQZ3 (Pages 3-18 and 29-47, this is Chapters 1 and 3) (35p)
- Chris Garrett. MicroPython: An Intro to Programming Hardware in Python https://realpython.com/micropython/ (14 pages)
- MicroPython Basics https://docs.arduino.cc/micropython/basics/micropython-basics/ (5 pages)
Recommended Reading before the course
- John D. Kelleher (2019) Deep Learning. https://mitpress.mit.edu/9780262537551/deep-learning/
- Yuli Vasiliev, Python for Data Science: A Hands-On Introduction, https://nostarch.com/python-data-science
- Tutorial on Jupyter Notebooks: https://www.datacamp.com/tutorial/tutorial-jupyter-notebook
- Alex Smola, and S. V. N. Vishwanathan (2008) Introduction to machine learning. Cambridge University, UK 32.34. https://alex.smola.org/drafts/thebook.pdf
Random Commands
pip install micropython-lsm6dsox
picotool.exe load -x C:\Users\ru42qak\AppData\Roaming\OpenMV\openmvide\firmware\ARDUINO_NANO_RP2040_CONNECT\firmware.bin
pip install jupyterlab
pip install everywhereml
python -m pip install jupyter
git clone https://github.com/goatchurchprime/jupyter_micropython_kernel.git
pip install -e jupyter_micropython_kernel
python -m notebook
python -m jupyter kernelspec list
C:\Users\ru42qak\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\LocalCache\local-packages\Python311\site-packages\jupyterlab>pip install -e jupyter_micropython_kernel
C:\Users\ru42qak\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\LocalCache\local-packages\Python311\site-packages\jupyterlab>python -m notebook