Overview
This Dust Sensor gives a good indication of the air quality in an environment by measuring the dust concentration. The Particulate Matter level (PM level) in the air is measured by counting the Low Pulse Occupancy time (LPO time) in given time unit. LPO time is proportional to PM concentration.
This sensor can provide reliable data for air purifier systems; it is responsive to PM of diameter 1μm.
Note: This sensor uses counting method to measure dust concentration, not weighing method, and the unit is pcs/L or pcs/0.01cf.
Tech specs
|
Item |
Norm |
Unit |
|
VCC |
4.75~5.75 |
V |
|
Standby Current Supply |
90 |
mA |
|
Detectable range of concentration |
0~28,000 / 0 ~ 8000 |
pcs/liter / pcs/0.01cf |
|
Operating Temperature Range |
0~45 |
°C |
|
Output Method |
Negative Logic, Digital output, High: over 4.0V(Rev.2), Low: under 0.7V |
- |
|
Detecting the particle diameter |
>1 |
μm |
|
Dimensions |
59(W) × 45(H) × 22(D) |
mm |
|
Humidity Range |
95% rh or less |
- |
Pinmaping
|
Arduino UNO |
Dust Sensor Pin |
Cable Color |
|
5V |
Pin 3 |
Red wire |
|
GND |
Pin 1 |
Black wire |
|
D8 |
Pin 4 |
Yellow wire |
Get Inspired
This project shows how to create a security system using the camera of an Arduino Nicla Vision board. The system automatically triggers a camera snapshot when presence is detected. Presence is detected when the system detects a sound level that exceeds a configurable threshold. The whole system is controlled by an Arduino Cloud dashboard.
Shortly after attending a recent tinyML workshop in Sao Paolo, Brazil, Joao Vitor Freitas da Costa was looking for a way to incorporate some of the technologies and techniques he learned into a useful project. Given that he lives in an area which experiences elevated levels of pickpocketing and automotive theft, he turned his attention to a smart car security system. His solution to a potential break-in or theft of keys revolves around the incorporation of an Arduino Nicla Vision board running a facial recognition model that only allows the vehicle to start if the owner is sitting in the driver’s seat. The beginning of the image detection/processing loop involves grabbing the next image from the board’s camera and sending it to a classification model where it receives one of three labels: none, unknown, or Joao, the driver. Once the driver has been detected for 10 consecutive seconds, the Nicla Vision activates a relay in order to complete the car’s 12V battery circuit, at which point the vehicle can be started normally with the ignition. Through this project, da Costa was able to explore a practical application of vision models at-the-edge to make his friend’s car safer to use. To see how it works in more detail, you can check out the video below and delve into the tinyML workshop he attended here.