Selecting the best model for monitoring industrial machines is challenging due to limited data and varying conditions. While deep learning (DL) is often considered ideal, it does not always perform well. We compared DL with traditional methods focused on feature extraction and classification. Our tests on seven datasets revealed that traditional methods are more reliable when machine conditions change, highlighting the importance of simpler, interpretable models for industrial monitoring.

We present a new method for creating flexible temperature sensors that can be applied to complex shapes. By using a technique called water transfer printing, these sensors can be transferred directly onto surfaces without the need for flexible films . This allows them to be more accurate, more reliable, and more adaptable than traditional sensors.  These sensors can work well in various environments and are unaffected by humidity, making them ideal for use in healthcare, robotics, and wearable devices.

We present a sensor with an original architecture for gas sensing that is 4 times more efficient than other sensors based on the same detection technology. It has a small volume (less than 1 cm³), low cost, and low power consumption, making it possible for it to be used in compact systems and even in smartphones. It is able to detect small changes in carbon dioxyde concentrations in ambient air for indoor air quality monitoring.