Alessandro Fontanella

TL;DR: The work presented focuses on the design of embedded software for wearable devices that are connected in wireless mode to a remote monitoring system for fall detection with tri-axial accelerometers.

TL;DR: The work proposes the embedding of a recurrent neural network (RNN) architecture on a micro controller unit (MCU) fed by tri-axial accelerometers data recorded by onboard sensors, and presents the design of an embedded software for wearable devices that are connected in wireless mode to a remote monitoring system.

TL;DR: This paper describes the development of a parallel implementation of the Support Vector Machines (SVMs) algorithm employed for the classification of hyperspectral images of in vivo human brain tissue, which is capable to perform efficient training and real-time compliant classification.

TL;DR: The proposed solutions exploit multi-core processors and graphic processing units for achieving real-time performance of the Harsanyi–Farrand–Chang method for virtual dimensionality estimation, together with better performance than other works in the literature.

TL;DR: This paper focuses on BP algorithms based on the following parameters: signal-to-noise ratio, kurtosis, entropy, information divergence, variance and linearly constrained minimum variance that can be derived using OpenMP and NVIDIA’s compute unified device architecture.