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| − | This course is designed as a week-long tutorial to engage with ubiquitous devices in the domain of smart environments and how to use machine learning to build smart devices. Here, we use an [[Arduino_Nano_RP2040_Connect|Arduino Nano RP2040 Connect]]. | + | This course is designed as a week-long tutorial to engage with ubiquitous devices in the domain of smart environments and how to use machine learning to build smart devices. Here, we use an Arduino Nano RP2040 Connect (https://store.arduino.cc/products/arduino-nano-rp2040-connect). |
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| − | = Final Projects = | + | = Link Page = |
| − | | + | https://www.sketching-with-hardware.org/wiki/UBISS2024-Links |
| − | == StressLess Shell ==
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| − | See the teaser video on [https://www.youtube.com/watch?v=NBSCIGqiXqM YouTube] by Songyan Teng, Jingyao Zheng, and Tim Zindulka.
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| − | | |
| − | == Plant Monitoring and Warning System ==
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| − | See the teaser video on [https://www.youtube.com/watch?v=DxjguBbUobk YouTube] by Shenxiu Wu, and Huong Nguyen.
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| − | | |
| − | == IntelliPen ==
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| − | The pen that can recognize the characters that you write! See the teaser video on [https://www.youtube.com/watch?v=WdLBq__ORBQ YouTube] by Mohammed Khalili, and Ali Mahmoudi.
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| − | | |
| − | == Hand Gesture Recognition ==
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| − | Find the code and documentation at [https://github.com/mamadzebal/Morse-Code-Detector GitHub]. The project was completed by Mohammed Farhoudi and Samira Kamali Poorazad.
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| − | | |
| − | = Schedule =
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| − | == Day 1 ==
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| − | * 16:00-17:00 Lecture: Introduction & Creating Interactive Smart Objects and Environments
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| − | * 17:00-17:45 Hands-On: [[UBISS2024#Task 1: Getting Started|Task 1: Getting Started]] Components, tools, and development environments
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| − | * 17:45-18:00 Lecture: Preview of the Tasks Ahead
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| − | | |
| − | == Day 2 ==
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| − | * 10:00-10:45 Lecture: Desiging and Implementing Sensor-Based Systems
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| − | * 10:45-11:00 ML Development Cycle: data collection, data cleaning, and labeling, selection of the AI/ML approach, hyper-parameter * selection and implementing the model, training the model/system, deploying the model, operations, re-training/continuous improvement
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| − | * 11:00-12:00 Hands-On: [[UBISS2024#Task 2: Read Data|Task 2: Read Data]] (accelerometer and analog pin)
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| − | * 12:00-13:00 lunch break
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| − | * 13:00-14:00 Hands-On: Task 3: Record the accelerometer data for four different actions (labeled dataset), store it, and transfer it to PC using the Arduino IDE
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| − | * 14:00-14:30 Lecture: Rule-based Systems: how to design them, pros: explainability, cons: it is hard
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| − | * 14:30-15:00 Hands-On: Task 4: analyze the data in Excel/Google sheets and find rules for the 4 actions
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| − | * 15:00-15:45 Hands-On: Task 5: Implement your rule-based algorithm, optional include explanations of why the state is recognized using the Arduino IDE
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| − | * 15:45-16:00 get Coffee / break
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| − | * 16:00-17:00 Ideation and testing ideas
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| − | * 17:00-17:15 Present your ideas
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| − | * 17:15-18:00 Hands-On: Discussion and presenting the results of Task 4 and 5
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| − | | |
| − | == Day 3 ==
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| − | * 10:00-10:30 Lecture: Introduction to Jupyter Notebooks, training an ML model based on a given data and the self-recorded data set on the PC
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| − | * 10:30-11:00 Lecture: Introduction to ML Libraries (everywhereML)
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| − | * 11:00-12:00 Hands-On: Project specification, Ideation on Project Ideas; and discussion of project ideas, group forming (groups of 2 or 3), Make your groups, specify your projects, see if you get the components (refine to work with the components available)
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| − | * 12:00-13:00 Lunch break
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| − | * 13:00-15:30 Hands-On: [[UBISS2024#Task 6: Getting Started with Jupyter Notebook|Task 6: Getting Started with Jupyter Notebook]] Installing Jupyter Notebook for Micropython, controlling LED, reading data, storing data
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| − | * 15:30-16:00 Coffee break
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| − | * 16:00-16:30 Hands-On: Presentation: status update on your project
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| − | * 16:15-18:00 Hands-On: [[UBISS2024#Task 7: Deploy Machine Learning Models|Task 7: Deploy Machine Learning Models]] Implementing a basic model using everywhereML
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| − | | |
| − | == Day 4 ==
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| − | * 10:00-10:45 Hands-On: Definition of project, project outline
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| − | * 10:45-11:15 Hands-On: project presentation: 60 sec per team
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| − | * 11:15-12:00 Hands-On: project work
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| − | * 12:00-13:00 Lunch break
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| − | * 13:00-15:00 Hands-On: project work
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| − | * 15:00-15:30 Hands-On: stand-up meeting on project progress
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| − | * 15:30-16:00 Coffee break
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| − | * 16:00-16:15 Lecture: How to run your system of a battery (see [[Tutorial AutoRun]]).
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| − | * 16:15-17:30 Hands-On: project work
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| − | * 17:30-18:00 Lecture: How to Evaluate ML Solutions (talk and discussion)
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| − | | |
| − | == Day 5 ==
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| − | * 10:00-10:30 Hands-On: stand-up meeting — project challenges and solutions
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| − | * 10:30-11:30 Hands-On: project work and preparing the presentation
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| − | ** [[UBISS2024#Requirements_for_the_Final_Presentation | Requirements for the Presentation]]
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| − | | |
| − | * 11:30-12:00 Lecture: Pitfalls and Challenges in Developing ML/AI for IoT
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| − | * 12:00-13:00 Lunch break
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| − | * 13:00-15:30 Hands-On: project work
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| − | * 15:30-16:00 Coffee break
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| − | * 16:00-16:30 Lecture: Testing and Reporting ML Performance (How to Test the Prototype? & How to Report Performance?)
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| − | * 16:30-17:30 Hands-On: Testing of prototype performance
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| − | * 17:30-18:00 Hands-On: Open issues for the presentation on Saturday, Feedback sessions
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| − | | |
| − | == Day 6 ==
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| − | * 13:15-18:15: Workshop Result Presentations
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| − | * 18:30-18:50: Debriefing
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| − | | |
| − | == Requirements for the Final Presentation ==
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| − | * The presentation has to be 4 minutes long (we stop you after 4 minutes!)
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| − | * First slide: Your team name and your names - and if you want a photo of the team
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| − | * A short video of the tech you envision (up to 60 sec, [https://www.kickstarter.com/ Kickstarter]-style promotion type)
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| − | * A technology description, including the list of components used in the prototype
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| − | * A description of your data set and how it was acquired
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| − | * The ML model/approach you took to learning the data
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| − | * An evaluation of how well your ML model works with the data set (and optional in real live)
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| − | | |
| − | == Final Submissions ==
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| − | You have to upload your final submission to the drive. This should include:
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| − | * a video where you explain your technology
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| − | ** show the electronics components and name them
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| − | ** show the physical setup that you created
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| − | ** show the code you wrote and briefly explain it
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| − | * a zip file with all the code that is used in your project
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| − | * a schematic / drawing of your system as PDF or image (drawing it on paper and making a photo is fine)
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| − | * your final presentation (as PDF, Powerpoint)
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| − | * [optional] a drawing of your system architecture (hand drawing is fine)
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| | | | |
| | = Tasks = | | = Tasks = |
| | | | |
| − | == Task 1: Getting Started == | + | == Task 0: connect an Arduino Nano RP2040 Connect board == |
| − | * Connect an Arduino Nano RP2040 Connect board, for this see [[Arduino_Nano_RP2040_Connect#Install the Arduino Nano RP2040 Connect Firmware|Install the Arduino Nano RP2040 Connect Firmware]]
| + | * Install the basic software https://labs.arduino.cc/en/labs/micropython |
| − | * if you use Linux/MacOS and there are issues, look for serial line permissions, e.g. https://www.xanthium.in/linux-serial-port-programming-using-python-pyserial-and-arduino-avr-pic-microcontroller and https://github.com/arduino/lab-micropython-editor/issues/64
| + | * connect the board via USB |
| − | * Install the Arduino Lab for MicroPython development environment, https://labs.arduino.cc/en/labs/micropython | + | * Make the orange LED (pin 6) blink using micro python https://docs.arduino.cc/micropython/basics/digital-analog-pins/ |
| − | * Task 1.1: 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 |
| − | * Task 1.2: Connect an external RGB LED (pin D2 = GPIO25, D3 = GPIO15, D4 = GPIO16) and control it (on, off, mix color, brightness), https://www.sketching-with-hardware.org/wiki/RGB_LED | + | * Control the external RGB LED (on, off, mix color, brightness) |
| − | * Task 1.3: Add the photo resistors to your board, read their values, and write them to the file; see the instructions for [[LDR]]. | |
| − | * Task 1.4: Combine your [[LDR]] and the [[RGB_LED]] example to change the blinking interval with the light value measures.
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| | | | |
| − | === Solution Task 1.1: LED Blinking === | + | === Solution Task 0.1: LED Blinking === |
| | <syntaxhighlight lang="python" line='line'> | | <syntaxhighlight lang="python" line='line'> |
| | # Blinky example | | # Blinky example |
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| | </syntaxhighlight> | | </syntaxhighlight> |
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| − | === Solution Task 1.2 Control external RGB === | + | === Solution Task 0.2 Control external RGB === |
| | <syntaxhighlight lang="python" line='line'> | | <syntaxhighlight lang="python" line='line'> |
| | # RGB example | | # RGB example |
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| | from machine import Pin | | from machine import Pin |
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| − | # RGB LED connected to the RP2040 | + | # RGB LED connected to Nano WiFi module. |
| − | ledG = Pin(25, Pin.OUT) | + | ledG = Pin(2, Pin.OUT) |
| − | ledR = Pin(15, Pin.OUT) | + | ledR = Pin(3, Pin.OUT) |
| − | ledB = Pin(16, Pin.OUT) | + | ledB = Pin(4, Pin.OUT) |
| | print("start") | | print("start") |
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| | </syntaxhighlight> | | </syntaxhighlight> |
| | | | |
| − | === Solution Task 1.3 Read Light-Dependent Resistor (LDR) === | + | == Task 1: read Acceleration from Arduino Nano RP2040 Connect board == |
| − | See [[LDR]]. A0 is the analog input with 16 bit resolution. It reads the analog value every second and print it to the console-
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| − | | |
| − | <syntaxhighlight lang="python" line='line'>
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| − | #Example usage for Arduino Nano
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| − | from machine import Pin, ADC
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| − | from time import sleep
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| − | | |
| − | analogPin = ADC(Pin(26))
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| − | | |
| − | while True:
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| − | analogVal16 = analogPin.read_u16()
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| − | print(analogVal16)
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| − | sleep(1)
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| − | </syntaxhighlight>
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| − | | |
| − | | |
| − | === Solution Task 1.4 Combine Light-Dependent Resistor (LDR) with Blinking LED ===
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| − | <syntaxhighlight lang="python" line='line'>
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| − | from machine import Pin, ADC
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| − | import time
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| − | | |
| − | led = Pin(6, Pin.OUT)
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| − | analogPin = ADC(Pin(26))
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| − | | |
| − | while (True):
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| − | analogVal16 = analogPin.read_u16()
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| − | print(analogVal16)
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| − | rate = analogVal16 / 300 # create a simple mapping
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| − | led.on()
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| − | time.sleep_ms(int(rate)) # convert the rate to an integer type
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| − | led.off()
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| − | time.sleep_ms(int(rate))
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| − | </syntaxhighlight>
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| − | | |
| − | == Task 2: Read Data ==
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| | * read data from the accelerometer and the gyro and print them (Arduino IDE) https://docs.arduino.cc/micropython/basics/board-examples/ | | * read data from the accelerometer and the gyro and print them (Arduino IDE) https://docs.arduino.cc/micropython/basics/board-examples/ |
| − | * extend your program to write the data from the accelerometers to a file; see the instructions for [[FileIO]]. | + | * 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 | | * transfer the file to your computer |
| − | * optional: add the photo resistors to your board, read their values, and write them to the file; see the instructions for [[LDR]]. | + | * 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 Task 2.1: Read Accelerometer and Gyro === | + | === Solution Task 1.1: Read Accelerometer and Gyro === |
| | <syntaxhighlight lang="python" line='line'> | | <syntaxhighlight lang="python" line='line'> |
| | import time | | import time |
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| | </syntaxhighlight> | | </syntaxhighlight> |
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| − | === Solution Task 2.2: Read analog values === | + | |
| − | A0 is the analog input with 16-bit resolution. It reads the analog value every second and print it to the console- | + | === Solution Task 1.2: 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- |
| | | | |
| | <syntaxhighlight lang="python" line='line'> | | <syntaxhighlight lang="python" line='line'> |
| | + | #Example usage for Arduino Nano |
| | from machine import Pin, ADC | | from machine import Pin, ADC |
| | from time import sleep | | from time import sleep |
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| | </syntaxhighlight> | | </syntaxhighlight> |
| | | | |
| − | == Task 6: Getting Started with Jupyter Notebook == | + | == Task 2: Jupyter Notebook == |
| − | * Connect the board | + | * connect the board |
| − | * Install the [[Jupyter Notebook]], | + | * install the Juypter Notebook, https://www.sketching-with-hardware.org/wiki/Jupyter |
| − | * Read the accelerometer and the gyro and show it in the notebook | + | * read the accelerometer and the gyro and show it in the notebook |
| | | | |
| − | === Task 6.1: is it moved? === | + | |
| | + | === Task 2.1: is it moved? === |
| | * read acceleration and gyro | | * read acceleration and gyro |
| | * calculate the differences between values | | * calculate the differences between values |
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| | * create a file on the device that logs, when it is moved | | * create a file on the device that logs, when it is moved |
| | | | |
| − | === Task 6.2: it was turned upside down? === | + | === Task 2.2: it was turned upside down? === |
| | * read acceleration and gyro | | * read acceleration and gyro |
| | * make a rule based "AI" that records | | * make a rule based "AI" that records |
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| | ** it was moved "quickly" | | ** it was moved "quickly" |
| | | | |
| − | == Task 7: Deploy Machine Learning Models == | + | == Task 3: ML on Arduino Nano Connect RP2040 == |
| | We will use https://github.com/eloquentarduino/everywhereml to detect the same gestures as in Task 2.2. For this, install everywhereml: | | We will use https://github.com/eloquentarduino/everywhereml to detect the same gestures as in Task 2.2. For this, install everywhereml: |
| − |
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| − | See [[EverywhereML]] for downloading the full example and a dataset to experiment with.
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| − |
| |
| | <syntaxhighlight lang="Bash"> | | <syntaxhighlight lang="Bash"> |
| − | pip3 install -U everywhereml | + | pip3 install -U everywhere |
| | </syntaxhighlight> | | </syntaxhighlight> |
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| | </syntaxhighlight> | | </syntaxhighlight> |
| | | | |
| − | == Task 8: Connect to WiFi == | + | == Task 4: connect both boards to WIFI == |
| − | This is an optional task.
| + | * connect both boards to WIFI using [[Tutorial_Network]] |
| − | * Connect both boards to WIFI using [[Tutorial Network]] | + | * use the Arduino Nano RP2040 Connect as output (showing a color) |
| − | * Use the Arduino Nano RP2040 Connect as output (showing a color) | + | * use the Arduino Nano RP2040 Connect as input (recognize with rules 3 gestures) |
| − | * Use the Arduino Nano RP2040 Connect as input (recognize with rules 3 gestures) | |
| | | | |
| | = Links = | | = Links = |
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| | C:\Users\ru42qak\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\LocalCache\local-packages\Python311\site-packages\jupyterlab>python -m notebook | | C:\Users\ru42qak\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\LocalCache\local-packages\Python311\site-packages\jupyterlab>python -m notebook |
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| − | [[Category:Courses]] | + | [[Category:Course]] |
| | [[Category:UBISS2024]] | | [[Category:UBISS2024]] |
| − | [[Category:Arduino Nano RP2040 Connect]]
| |
| − | [[Category:MicroPython]]
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