Difference between revisions of "UBISS2024"
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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
Contents
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
1 # Blinky example
2
3 import time
4 from machine import Pin
5
6 # This is the only LED pin available on the Nano RP2040,
7 # other than the RGB LED connected to Nano WiFi module.
8 led = Pin(6, Pin.OUT)
9
10 while (True):
11 led.on()
12 time.sleep_ms(250)
13 led.off()
14 time.sleep_ms(200)
solution Project 0: Control external RGB
1 # RGB example
2
3 import time
4 from machine import Pin
5
6 # RGB LED connected to Nano WiFi module.
7 ledG = Pin(2, Pin.OUT)
8 ledR = Pin(3, Pin.OUT)
9 ledB = Pin(4, Pin.OUT)
10 print("start")
11
12 while (True):
13 print("*")
14 ledG.on()
15 ledR.off()
16 ledB.off()
17 time.sleep_ms(250)
18 ledG.off()
19 ledR.on()
20 ledB.off()
21 time.sleep_ms(250)
22 ledG.off()
23 ledR.off()
24 ledB.on()
25 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
1 import time
2 from lsm6dsox import LSM6DSOX
3
4 from machine import Pin, I2C
5 lsm = LSM6DSOX(I2C(0, scl=Pin(13), sda=Pin(12)))
6
7 while (True):
8 accel_data = lsm.accel()
9 print('Accelerometer: x:{:>8.3f} y:{:>8.3f} z:{:>8.3f}'.format(*accel_data))
10 gyro_data = lsm.gyro()
11 print('Gyroscope: x:{:>8.3f} y:{:>8.3f} z:{:>8.3f}'.format(*gyro_data))
12 print("")
13 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-
1 #Example usage for Arduino Nano
2 from machine import Pin, ADC
3 from time import sleep
4
5 analogPin = ADC(Pin(26))
6
7 while True:
8 analogVal16 = analogPin.read_u16()
9 print(analogVal16)
10 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