Papers by Pasquale Fedele
Lecture Notes in Computer Science, 2014
The "BrainControl Basic Communicator" (BrainControl BC) is an augmentative and alternative commun... more The "BrainControl Basic Communicator" (BrainControl BC) is an augmentative and alternative communication (AAC) system based on Braincomputer interface (BCI) technology. The system has been designed for patients with severe disabilities due to pathologies such as Amyotrophic Lateral Sclerosis (ALS), Multiple Sclerosis, ischemic or traumatic injuries. The first prototype of the BrainControl BC was completed in mid 2012 and has been completed in the mid 2013. From 2012, 20 locked-in patients have been trained with success and in the last few months 12 of these patients are continuously using the system, as it represents the only possibility to communicate. BrainControl BC is part of the BrainControl project, aiming to develop a BCI platform that allows people suffering from sever disabilities to overcome physical and communicative impairments. In particular, BrainControl can help patients suffering from diseases that paralyze the whole body or parts of the body, but who retain their intellectual abilities. Future versions of BrainControl, which are currently under development, will include advanced communication and functionalities, home automation, the control of a wheelchair and robotics.
Environmental Pollution, 2012
Deterministic photochemical air quality models are commonly used for regulatory management and pl... more Deterministic photochemical air quality models are commonly used for regulatory management and planning of urban airsheds. These models are complex, computer intensive, and hence are prohibitively expensive for routine air quality predictions. Stochastic methods are becoming increasingly popular as an alternative, which relegate decision making to artificial intelligence based on Neural Networks that are made of artificial neurons or 'nodes' capable of 'learning through training' via historic data. A Neural Network was used to predict particulate matter concentration at a regulatory monitoring site in Phoenix, Arizona; its development, efficacy as a predictive tool and performance vis-à-vis a commonly used regulatory photochemical model are described in this paper. It is concluded that Neural Networks are much easier, quicker and economical to implement without compromising the accuracy of predictions. Neural Networks can be used to develop rapid air quality warning systems based on a network of automated monitoring stations.
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Papers by Pasquale Fedele