Edge AI | Technology Tutorials

In Lesson 6, I gave you a homework challenge: build a dimmable LED using a potentiometer. In today’s Lesson 7, we go through the solution together step-by-step.

This lesson is all about taking analog input from a potentiometer and converting it into smooth PWM output to control the brightness of an LED. It’s a very practical project because it teaches you how to read real-world analog values and turn them into useful control signals — skills we’ll use again and again as we build smarter AI-powered projects.

In the video, I walk you through the complete working code. You’ll see how we read the potentiometer value (0 to 4095), convert that raw number into a proper brightness percentage using a bit of math (with a nice logarithmic curve so the brightness feels natural to the human eye), and then send that value to the LED using PWM. The result is a very smooth, responsive dimmer that feels professional.

Even though this seems like a simple project, it’s actually an important stepping stone. Understanding how to read sensors and smoothly control outputs is fundamental to building real AI on the Edge systems — whether you’re controlling motors, adjusting screen brightness, or varying the speed of a robot based on sensor input.

By the end of this lesson, you should have a solid understanding of how to combine the ADC (Analog to Digital Converter) with PWM output, and more importantly, how to think about mapping real-world inputs to useful outputs.

So if you did the homework, great job! If you got stuck, don’t worry — we go through the full solution together. And as always, I strongly encourage you to take the code and make it your own. Try changing the response curve, add multiple LEDs with different colors, or combine it with things we’ve learned in earlier lessons.

This is the kind of foundational hardware skill that will serve you well as we continue moving deeper into the AI on the Edge class. You’re doing great — keep going!

We are still using the schematic from our earlier project.

Fusion Hat Circuit Diagram — This is the circuit we will use moving forward in the class

In this lesson, this is the code which we came up with:

#!/usr/bin/env python3
from fusion_hat.adc import ADC
from fusion_hat.pwm import PWM
from time import sleep
import math
potPin=0
myPot = ADC(0)
redPin=5
redLED=PWM(redPin)

while True:
    potVal = myPot.read()                     # 0 to 4095
    writeVal=100**(potVal/4095)-.005
    redLED.pulse_width_percent(int(writeVal))
    print(potVal,writeVal)
    sleep(0.1)

#!/usr/bin/env python3

from fusion_hat.adc import ADC

from fusion_hat.pwm import PWM

from time import sleep

import math

potPin=0

myPot = ADC(0)

redPin=5

redLED=PWM(redPin)

while True:

potVal = myPot.read() # 0 to 4095

writeVal=100**(potVal/4095)-.005

redLED.pulse_width_percent(int(writeVal))

print(potVal,writeVal)

sleep(0.1)

In today’s Lesson we will see just how far we can push things on the Raspberry Pi 5. I will show you how to install YOLO11 on the Pi . I will show you a simple program that will run YOLO11 under Python and openCV. The objective in today’s lesson is to see if the Pi5, without a Hailo accelerator hat, has sufficient power to do useful object detection. We will not use an accelerator hat, but the work is computationally intensive, so you must use active cooling. This is the low cost cooling fan we are using. It is sufficient to do the job, low cost and is a thin form factor that allows other hats to still fit on the Raspberry Pi 5. You can pick up the fan I am using HERE. Also, we are using an 8GB Pi 5. If you already have a Pi 5, it will probably work. The Pi 5 we are using is available HERE. These appliations are power hungry, so make sure you are using an official Pi Power supply.

In this lesson, I assume you are already familiar with the Pi 5. Note we are using Bookworm OS. Not all the dependencies work yet on Trixie, so I strongly recommend starting by flashing a fresh bookworm SD card.

YOLO11 is a powerful AI object detection model that runs well on the Raspberry Pi 5. The model below:

# 1. Configure X11 (manual steps required)
sudo raspi-config
# → Go to Advanced Options → X11 → Enable X11
# → Finish and reboot when prompted

# 5. Update system and install OpenCV
sudo apt update
sudo apt full-upgrade -y
sudo apt install python3-opencv -y

# Verify OpenCV
python3 -c "import cv2; print('OpenCV version:', cv2.__version__)"
# Expected output: something like "OpenCV version: 4.6.0" or higher

# 6. Install MediaPipe
pip install mediapipe --break

# Verify MediaPipe
python3 -c "import mediapipe as mp; print('MediaPipe version:', mp.__version__)"

# 7. Create and activate virtual environment for YOLO11 (Ultralytics)
python3 -m venv --system-site-packages YOLO
source YOLO/bin/activate

# You are now inside the (YOLO) virtual environment
# Install Ultralytics YOLO11 inside it
pip install "numpy<2.0" ultralytics

# Now create a Pi friendly YOLO11 model
yolo export model=yolo11n.pt format=ncnn

# Optional: Verify YOLO installation
python -c "from ultralytics import YOLO; print('Ultralytics YOLO ready')"

# When finished working with YOLO, you can deactivate with:
# deactivate

#Now open Thonny, and you need to point thonny to the virtual environment you 
#just created. Open tools- options, select 'interpreter' tab, then click they Python
#executable, selecting ... and navigate from home directory, 
#to YOLO, to bin, and then select python

# 1. Configure X11 (manual steps required)

sudo raspi-config

# → Go to Advanced Options → X11 → Enable X11

# → Finish and reboot when prompted

# 5. Update system and install OpenCV

sudo apt update

sudo apt full-upgrade -y

sudo apt install python3-opencv -y

# Verify OpenCV

python3 -c "import cv2; print('OpenCV version:', cv2.__version__)"

# Expected output: something like "OpenCV version: 4.6.0" or higher

# 6. Install MediaPipe

pip install mediapipe --break

# Verify MediaPipe

python3 -c "import mediapipe as mp; print('MediaPipe version:', mp.__version__)"

# 7. Create and activate virtual environment for YOLO11 (Ultralytics)

python3 -m venv --system-site-packages YOLO

source YOLO/bin/activate

# You are now inside the (YOLO) virtual environment

# Install Ultralytics YOLO11 inside it

pip install "numpy<2.0" ultralytics

# Now create a Pi friendly YOLO11 model

yolo export model=yolo11n.pt format=ncnn

# Optional: Verify YOLO installation

python -c "from ultralytics import YOLO; print('Ultralytics YOLO ready')"

# When finished working with YOLO, you can deactivate with:

# deactivate

#Now open Thonny, and you need to point thonny to the virtual environment you

#just created. Open tools- options, select 'interpreter' tab, then click they Python

#executable, selecting ... and navigate from home directory,

#to YOLO, to bin, and then select python

Now you should be set up to use YOLO11 on the Raspberry Pi 5!

We will start with this program, which is a simple grab a frame and show a frame openCV Program

import cv2
from picamera2 import Picamera2
import time
piCam = Picamera2()
W=1280
H=720
RES = (W,H)
piCam.preview_configuration.main.size = RES
piCam.preview_configuration.main.format = "RGB888"
piCam.preview_configuration.controls.FrameRate=60
piCam.preview_configuration.align()
piCam.configure("preview")
piCam.start()
fps=0

tStart=time.time()
while True:
    frame= piCam.capture_array()
    #frame=cv2.flip(frame,-1)
    deltaT=time.time()-tStart
    tStart=time.time()
    fps= fps*.9 + .1/deltaT
    cv2.putText(frame, "FPS: "+str(round(fps,1)), (int(W*.01), int(H*.075)), 
            cv2.FONT_HERSHEY_SIMPLEX, H*.002, (0, 0, 255), 2)
    cv2.imshow("Camera", frame)
    cv2.moveWindow("Camera",100,100)
    if cv2.waitKey(1)==ord('q'):
        break
cv2.destroyAllWindows()

import cv2

from picamera2 import Picamera2

import time

piCam = Picamera2()

W=1280

H=720

RES = (W,H)

piCam.preview_configuration.main.size = RES

piCam.preview_configuration.main.format = "RGB888"

piCam.preview_configuration.controls.FrameRate=60

piCam.preview_configuration.align()

piCam.configure("preview")

piCam.start()

fps=0

tStart=time.time()

while True:

frame= piCam.capture_array()

#frame=cv2.flip(frame,-1)

deltaT=time.time()-tStart

tStart=time.time()

fps= fps*.9 + .1/deltaT

cv2.putText(frame, "FPS: "+str(round(fps,1)), (int(W*.01), int(H*.075)),

cv2.FONT_HERSHEY_SIMPLEX, H*.002, (0, 0, 255), 2)

cv2.imshow("Camera", frame)

cv2.moveWindow("Camera",100,100)

if cv2.waitKey(1)==ord('q'):

break

cv2.destroyAllWindows()

In the video, we show how to use YOLO11 object detection in this simple program.

import cv2
from ultralytics import YOLO
from picamera2 import Picamera2
import time
piCam = Picamera2()
W=1280
H=720
RES = (W,H)
piCam.preview_configuration.main.size = RES
piCam.preview_configuration.main.format = "RGB888"
piCam.preview_configuration.controls.FrameRate=60
piCam.preview_configuration.align()
piCam.configure("preview")
piCam.start()
# Load the exported NCNN model (replace with your model path)
model = YOLO("/home/pjm/yolo11n_ncnn_model", task = 'detect')
fps=0
tStart=time.time()
# Set resolution for faster processing (optional, adjust based on your needs)
while True:
    frame= piCam.capture_array()
    results = model(frame, conf=0.25, verbose=False)
    frame = results[0].plot()  # Plots old boxes on new frame!
    deltaT=time.time()-tStart
    tStart=time.time()
    fps= fps*.8 + .2/deltaT
    cv2.putText(frame, "FPS: "+str(round(fps,1)), (int(W*.01), int(H*.075)), 
            cv2.FONT_HERSHEY_SIMPLEX, H*.002, (0, 0, 255), 2)
    
    # Display the result
    cv2.imshow("YOLO11 Detection", frame)

    # Exit on 'q' key
    if cv2.waitKey(1) == ord('q'):
        break

# Cleanup
cv2.destroyAllWindows()
import gc
gc.collect()  # Force garbage collection to reclaim memory/connections
#Make Extra sure all processes are killed
import os
os._exit(0)
print("Program Terminated")

import cv2

from ultralytics import YOLO

from picamera2 import Picamera2

import time

piCam = Picamera2()

W=1280

H=720

RES = (W,H)

piCam.preview_configuration.main.size = RES

piCam.preview_configuration.main.format = "RGB888"

piCam.preview_configuration.controls.FrameRate=60

piCam.preview_configuration.align()

piCam.configure("preview")

piCam.start()

# Load the exported NCNN model (replace with your model path)

model = YOLO("/home/pjm/yolo11n_ncnn_model", task = 'detect')

fps=0

tStart=time.time()

# Set resolution for faster processing (optional, adjust based on your needs)

while True:

frame= piCam.capture_array()

results = model(frame, conf=0.25, verbose=False)

frame = results[0].plot() # Plots old boxes on new frame!

deltaT=time.time()-tStart

tStart=time.time()

fps= fps*.8 + .2/deltaT

cv2.putText(frame, "FPS: "+str(round(fps,1)), (int(W*.01), int(H*.075)),

cv2.FONT_HERSHEY_SIMPLEX, H*.002, (0, 0, 255), 2)

# Display the result

cv2.imshow("YOLO11 Detection", frame)

# Exit on 'q' key

if cv2.waitKey(1) == ord('q'):

break

# Cleanup

cv2.destroyAllWindows()

import gc

gc.collect() # Force garbage collection to reclaim memory/connections

#Make Extra sure all processes are killed

import os

os._exit(0)

print("Program Terminated")

Technology Tutorials

Tag Archives: Edge AI

AI on the Edge LESSON 7: Homework Solution for Dimmable LED

AI on the Edge: Install and Run YOLO Object Detection on the Raspberry Pi 5

Making The World a Better Place One High Tech Project at a Time. Enjoy!