GIANOGLIO , Christian

Variable energy constraints affect the implementations of neural networks on battery-operated embedded systems. This paper describes a learning algorithm for randomization-based neural networks with hard-limit activation functions. The approach adopts a novel cost function that balances accuracy and network complexity during training. From an...

The availability of compact digital circuitry for the support of neural networks is a key requirement for resource-constrained embedded systems. This brief tackles the implementation of single hidden-layer feedforward neural networks, based on hard-limit activation functions, on reconfigurable devices. The resulting design strategy relies on a...

In this letter, we present a tactile sensing system based on piezoelectric sensors, embedded electronics, and a machine learning (ML)-based approach for hardness discrimination. Various statistical features were extracted and evaluated through machine learning algorithms including support vector machines (SVM), single-layer feed-forward neural...

No description

No description

The development of automated tools capable of monitoring electric motors is important for industrial applications. The measure of partial discharges is one of the most prominent methodologies for the evaluation of electric motors operating conditions. This work proposes to apply tensor-based classification methods to discriminate between...