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Barcelona, Spain

Viaud-Delmon I.,University Pierre and Marie Curie | Gaggioli A.,Catholic University of the Sacred Heart | Ferscha A.,Johannes Kepler University | Dunne S.,STARLAB
Studies in Health Technology and Informatics | Year: 2012

Human computer confluence (HCC) is an ambitious research program studying how the emerging symbiotic relation between humans and computing devices can enable radically new forms of sensing, perception, interaction, and understanding. It is an interdisciplinary field, bringing together researches from horizons as various as pervasive computing, bio-signals processing, neuroscience, electronics, robotics, virtual & augmented reality, and provides an amazing potential for applications in medicine and rehabilitation. © 2012 Interactive Media Institute and IOS Press. Source

Pierdicca N.,University of Rome La Sapienza | Guerriero L.,University of Rome Tor Vergata | Brogioni M.,University of Rome La Sapienza | Egido A.,STARLAB
International Geoscience and Remote Sensing Symposium (IGARSS) | Year: 2012

The work presented in this paper has been carried out with the aim of interpreting the data of a GNSS Reflectometer (GNSS-R) over land. The problem involves the analysis of bistatic scattering of the incoming signal collected around the specular direction. This requires to model the coherent component associated to the mean surface but at the same time the diffuse incoherent component due to roughness at wavelength scale. In presence of vegetation, both components will be affected, the former mainly because of the canopy attenuation and the latter for the combined effect of attenuation and volume scattering. The paper reviews the problem and presents the approach followed to develop a simulator of GNSS-R data over land, aiming to support potential applications of GNSS-R for soil moisture and biomass retrieval. © 2012 IEEE. Source

Molaee-Ardekani B.,French Institute of Health and Medical Research | Molaee-Ardekani B.,University of Rennes 1 | Marquez-Ruiz J.,Pablo De Olavide University | Merlet I.,French Institute of Health and Medical Research | And 10 more authors.
Brain Stimulation | Year: 2013

Although it is well-admitted that transcranial Direct Current Stimulation (tDCS) allows for interacting with brain endogenous rhythms, the exact mechanisms by which externally-applied fields modulate the activity of neurons remain elusive. In this study a novel computational model (a neural mass model including subpopulations of pyramidal cells and inhibitory interneurons mediating synaptic currents with either slow or fast kinetics) of the cerebral cortex was elaborated to investigate the local effects of tDCS on neuronal populations based on an in-vivo experimental study. Model parameters were adjusted to reproduce evoked potentials (EPs) recorded from the somatosensory cortex of the rabbit in response to air-puffs applied on the whiskers. EPs were simulated under control condition (no tDCS) as well as under anodal and cathodal tDCS fields. Results first revealed that a feed-forward inhibition mechanism must be included in the model for accurate simulation of actual EPs (peaks and latencies). Interestingly, results revealed that externally-applied fields are also likely to affect interneurons. Indeed, when interneurons get polarized then the characteristics of simulated EPs become closer to those of real EPs. In particular, under anodal tDCS condition, more realistic EPs could be obtained when pyramidal cells were depolarized and, simultaneously, slow (resp. fast) interneurons became de- (resp. hyper-) polarized. Geometrical characteristics of interneurons might provide some explanations for this effect. © 2013 Elsevier Inc. All rights reserved. Source

Paloscia S.,CNR Institute of Applied Physics Nello Carrara | Pettinato S.,CNR Institute of Applied Physics Nello Carrara | Santi E.,CNR Institute of Applied Physics Nello Carrara | Notarnicola C.,EURAC Research | And 2 more authors.
Remote Sensing of Environment | Year: 2013

The main objective of this research is to develop, test and validate a soil moisture content (SMC) algorithm for GMES Sentinel-1 characteristics. The SMC product, which is to be generated from Sentinel-1 data, requires an algorithm capable of processing operationally in near-real-time and delivering the product to the GMES services within 3. h from observation. An approach based on an Artificial Neural Network (ANN) has been proposed that represents a good compromise between retrieval accuracy and processing time, thus enabling compliance with the timeliness requirements. The algorithm has been tested and subsequently validated in several test areas in Italy, Australia, and Spain.In all cases the validation results were very much in line with GMES requirements (with RMSE generally <. 4%SMC - between 1.67%SMC and 6.68%SMC - and very low bias), except for the case of the test area in Spain, where the validation results were penalized by the availability of only VV polarized SAR images and MODIS low-resolution NDVI. Nonetheless, the obtained RMSE was slightly higher than 4%SMC. © 2013 Elsevier Inc. Source

Salvador R.,Institute of Biophysics and Biomedical Engineering IBEB | Mekonnen A.,Institute of Biophysics and Biomedical Engineering IBEB | Ruffini G.,STARLAB | Miranda P.C.,Institute of Biophysics and Biomedical Engineering IBEB
2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC'10 | Year: 2010

Much of our knowledge about the electric field distribution in transcranial current stimulation (tCS) still relies on results obtained from layered spherical head models. In this work we created a high resolution finite element model of a human head by segmentation of MRI images, and paid particular attention to the representation of the cortical sheet. This model was then used to calculate the electric field induced by two electrodes: an anode placed above the left motor cortex, and a cathode placed over the right eyebrow. The results showed that the maxima of the current density appear located on localized hotspots in the bottom of sulci and not on the cortical surface as would be expected from spherical models. This also applies to the components of the current density normal and tangential to the cortical surface. These results show that such highly detailed head models are needed to correctly predict the effects of tCS on cortical neurons. © 2010 IEEE. Source

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