An embedded toolset for human activity monitoring in critical environments.

In many working and recreational activities, there are scenarios where both individual and collective safety have to be constantly checked and properly signaled, as occurring in dangerous workplaces or during pandemic events like the recent COVID-19 disease. From wearing personal protective equipment to filling physical spaces with an adequate number of people, it is clear that a possibly automatic solution would help to check compliance with the established rules. Based on an off-the-shelf compact and low-cost hardware, we present a deployed real use-case embedded system capable of perceiving people's behavior and aggregations and supervising the appliance of a set of rules relying on a configurable plug-in framework. Working on indoor and outdoor environments, we show that our implementation of counting people aggregations, measuring their reciprocal physical distances, and checking the proper usage of protective equipment is an effective yet open framework for monitoring human activities in critical conditions.

Deep networks for behavioral variant frontotemporal dementia identification from multiple acquisition sources.

Behavioral variant frontotemporal dementia (bvFTD) is a neurodegenerative syndrome whose clinical diagnosis remains a challenging task especially in the early stage of the disease. Currently, the presence of frontal and anterior temporal lobe atrophies on magnetic resonance imaging (MRI) is part of the diagnostic criteria for bvFTD. However, MRI data processing is usually dependent on the acquisition device and mostly require human-assisted crafting of feature extraction. Following the impressive improvements of deep architectures, in this study we report on bvFTD identification using various classes of artificial neural networks, and present the results we achieved on classification accuracy and obliviousness on acquisition devices using extensive hyperparameter search. In particular, we will demonstrate the stability and generalization of different deep networks based on the attention mechanism, where data intra-mixing confers models the ability to identify the disorder even on MRI data in inter-device settings, i.e., on data produced by different acquisition devices and without model fine tuning, as shown from the very encouraging performance evaluations that dramatically reach and overcome the 90% value on the AuROC and balanced accuracy metrics.

A parallel architecture for interactively rendering scattering and refraction effects.

A new method for interactive rendering of complex lighting effects combines two algorithms. The first performs accurate ray tracing in heterogeneous refractive media to compute high-frequency phenomena. The second applies lattice-Boltzmann lighting to account for low-frequency multiple-scattering effects. The two algorithms execute in parallel on modern graphics hardware. This article includes a video animation of the authors' real-time algorithm rendering a variety of scenes.