In the traditional sensing setup, if used, the reservoir if used passively. In such “reservoir computing and sensing” approaches the reservoir is used to post-process the information that is generated by a separate sensor component. The reservoir can be safely replaced by an artificial neural network without any impact on the functionality of the sensor. However, in our “reservoir computing for sensing” approach, the reservoir is an integral part of the sensor. It cannot be replaced without worsening the performance of the sensor.
In the traditional sensing setup, the sensor-environment interaction has to be carefully engineered and it should be strong (to beat noise). In our setup, the sensor-environments interaction can be weak, not fully known, it does not be carefully engineered, and it can be possibly random, being suitable for embedded biotech IoT sensing applications.
The application possibilities and the socio-economic impact is enormous since sensors are ubiquitous in nearly all technological applications. We have demonstrated the concept within the academic environment. Our dissemination activities should make the ideas accessible to various types of potential lead users.
The novel way of sensing being developed in the project can be used to both design novel sensing applications, but also to re-use the existing ones in unexpected ways. We anticipate further impact on other industries, since the algorithm is universally applicable to several societal challenges where advanced neuromorphic, embedded, and unconventional information processing techniques could be used with a great advantage (energy consumption control, traffic jam prediction, transport logistic planning, security, medical applications, etc.)
