Power-efficientbrain-machine interfaces via brain-state estimation.

Advances in brain-machine interfaces (BMIs) can potentially improve the quality of life of millions of users with spinal cord injury or other neurological disorders by allowing them to interact with the physical environment at their will.To reduce the power consumption of the brain-implanted interface, this article presents the first hardware realization of anintention-aware interface via brain-state estimation.It is shown that incorporating brain-state estimation reduces thepower consumption and reduces total energy dissipation by over 1.

[Big data in environmental epidemiology. Satellite and land use data for the estimation of environmental exposures at national level].

Objectives: to define a national geographic domain, with high spatial (1 km²) and temporal (daily) resolution, and to build a list of georeferenced environmental and temporal indicators useful for environmental epidemiology applications at national level.

Design: geographic study.

Setting And Participants: study domain: Italian territory divided into 307,635 1-km² grid cells; study period: 2006-2012, divided into 2,557 daily time windows.

Estimation of daily PM concentrations in Italy (2006-2012) using finely resolved satellite data, land use variables and meteorology.

Health effects of air pollution, especially particulate matter (PM), have been widely investigated. However, most of the studies rely on few monitors located in urban areas for short-term assessments, or land use/dispersion modelling for long-term evaluations, again mostly in cities. Recently, the availability of finely resolved satellite data provides an opportunity to estimate daily concentrations of air pollutants over wide spatio-temporal domains.