Grove - Temperature & Humidity Sensor (SHT31)
Grove - Temp&Humi Sensor(SHT31) is a highly reliable, accurate, quick response and integrated temperature & humidity sensor.
Overview
The sensor(chip) used in the module is designed with Sensirion’s CMOSens® technology. The chip is well calibrated, linearized and compensated for digital output.
The typical accuracy of this module can be ±2%RH (for relative humidity) and ±0.3°C (for temperature).
This module is compatible with 3.3 Volts and 5 Volts and hence does not require a voltage level shifter. This module communicates using with I2C serial bus and can work up to 1 MHz speed. We also have provided a highly abstracted library to make this product more easier to use.
Using the sensor is easy.
For Seeeduino (compliant with Arduino), just connect this breakout board with the main control board via Grove cable.
Then use the provided library and example/demo code available at GitHub to get your data. If you’re using an Arduino without a Base Shield, simply connect the VIN pin to the 5V voltage pin, GND to ground, SCL to I2C Clock (Analog 5) and SDA to I2C Data (Analog 4).
Features:
- Highly reliable, accurate and quick response time
- Grove compatible and easy to use
- Well calibrated, linearized, compensated for digital output
- Highly abstracted development library
Get Inspired
As Jallson Suryo discusses in his project, adding voice controls to our appliances typically involves an internet connection and a smart assistant device such as Amazon Alexa or Google Assistant. This means extra latency, security concerns, and increased expenses due to the additional hardware and bandwidth requirements. This is why he created a prototype based on an Arduino Nicla Voice that can provide power for up to four outlets using just a voice command. Suryo gathered a dataset by repeating the words “one," “two," “three," “four," “on," and “off” into his phone and then uploaded the recordings to an Edge Impulse project. From here, he split the files into individual words before rebalancing his dataset to ensure each label was equally represented. The classifier model was trained for keyword spotting and used Syntiant NDP120-optimal settings for voice to yield an accuracy of around 80%. Apart from the Nicla Voice, Suryo incorporated a Pro Micro board to handle switching the bank of relays on or off. When the Nicla Voice detects the relay number, such as “one” or “three," it then waits until the follow-up “on” or “off” keyword is detected. With both the number and state now known, it sends an I2C transmission to the accompanying Pro Micro which decodes the command and switches the correct relay. To see more about this voice-controlled power strip, be sure to check out Suryo’s Edge Impulse tutorial.