Planet Arduino

When dealing with indoor climate controls, there are several variables to consider, such as the outside weather, people’s tolerance to hot or cold temperatures, and the desired level of energy savings. Windows can make this extra challenging, as they let in large amounts of light/heat and can create poorly insulated regions, which is why Jallson Suryo developed a prototype that aims to balance these needs automatically through edge AI techniques.

Suryo’s smart building ventilation system utilizes two separate boards, with an Arduino Nano 33 BLE Sense handling environmental sensor fusion and a Nicla Voice listening for certain ambient sounds. Rain and thunder noises were uploaded from an existing dataset, split and labeled accordingly, and then used to train a Syntiant audio classification model for the Nicla Voice’s NDP120 processor. Meanwhile, weather and ambient light data was gathered using the Nano’s onboard sensors and combined into time-series samples with labels for sunny/cloudy, humid, comfortable, and dry conditions.

After deploying the board’s respective classification models, Suryo added some additional code that writes new I2C data from the Nicla Voice to the Nano that indicates if rain/thunderstorm sounds are present. If they are, the Nano can automatically close the window via servo motors while other environmental factors can set the position of the blinds. With this multi-sensor technique, a higher level of accuracy can be achieved for more precision control over a building’s windows, and thus attempt to lower the HVAC costs.

More information about Suryo’s project can be found here on its Edge Impulse docs page

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The damage and destruction caused by structure fires to both people and the property itself is immense, which is why accurate and reliable fire detection systems are a must-have. As Nekhil R. notes in his write-up, the current rule-based algorithms and simple sensor configurations can lead to reduced accuracy, thus showing a need for more robust systems.

This led Nekhil to devise a solution that leverages sensor fusion and machine learning to make better predictions about the presence of flames. His project began with collecting environmental data consisting of temperature, humidity, and pressure from his Arduino Nano 33 BLE Sense’s onboard sensor suite. He also labeled each sample either Fire or No Fire using the Edge Impulse Studio, which was used to generate spectral features from the three time-series sensor values. This information was then passed along to a Keras neural network that had been configured to perform classification, resulting in an overall accuracy of 92.86% when run on real world test samples.

Confident in his now-trained model, Nekhil deployed his model as an Arduino library back to the Nano 33 BLE Sense. The Nano sends a message over its UART pins to an awaiting ESP8266-01 board when a fire has been detected. And in turn, the ESP8266 triggers an IFTTT webhook to alert the user via an email.

If you would like to learn more about the construction of this fire recognition system, plenty of details can be found on the project page.

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