Paleosecular variation of the magnetic field recorded in Pleistocene-holocene volcanics from Pantelleria (italy) and Azores archipelago (portugal): implications for local volcanic history

The primary goal of volcanological studies is to reconstruct the eruptive history of active volcanoes, by correlating and dating volcanic deposits, in order to depict a future scenario and determine the volcanic hazard of an area. However, alternative methods are necessary where the lack of outcrops, the deposit variability and discontinuity make the correlation difficult, and suitable materials for an accurate dating lack. In this thesis, paleomagnetism (a branch of Geophysics studying the remanent magnetization preserved in rocks) is used as a correlating and dating tool. The correlation is based on the assumption that coeval rocks record similar paleomagnetic directions; the dating relies upon the comparison between paleomagnetic directions recorded by rocks with the expected values from references Paleo-Secular Variation curves (PSV, the variation of the geomagnetic field along time). I first used paleomagnetism to refine the knowledge of the pre – 50 ka geologic history of the Pantelleria island (Strait of Sicily, Italy), by correlating five ignimbrites and two breccias deposits emplaced during that period. Since the use of the paleomagnetic dating is limited by the availability of PSV curves for the studied area, I firstly recovered both paleomagnetic directions and intensities (using a modified Thellier method) from radiocarbon dated lava flows in São Miguel (Azores Islands, Portugal), reconstructing the first PSV reference curve for the Atlantic Ocean for the last 3 ka. Afterwards, I applied paleomagnetism to unravel the chronology and characteristics of Holocene volcanic activity at Faial (Azores) where geochronological age constraints lack. I correlated scoria cones and lava flows yielded by the same eruption on the Capelo Peninsula and dated eruptive events (by comparing paleomagnetic directions with PSV from France and United Kingdom), finding that the volcanics exposed at the Capelo Peninsula are younger than previously believed, and entirely comprised in the last 4 ka.

Parallel modeling of the electric field distribution in the brain

The term "Brain Imaging" identi�es a set of techniques to analyze the structure and/or functional behavior of the brain in normal and/or pathological situations. These techniques are largely used in the study of brain activity. In addition to clinical usage, analysis of brain activity is gaining popularity in others recent �fields, i.e. Brain Computer Interfaces (BCI) and the study of cognitive processes. In this context, usage of classical solutions (e.g. f MRI, PET-CT) could be unfeasible, due to their low temporal resolution, high cost and limited portability. For these reasons alternative low cost techniques are object of research, typically based on simple recording hardware and on intensive data elaboration process. Typical examples are ElectroEncephaloGraphy (EEG) and Electrical Impedance Tomography (EIT), where electric potential at the patient's scalp is recorded by high impedance electrodes. In EEG potentials are directly generated from neuronal activity, while in EIT by the injection of small currents at the scalp. To retrieve meaningful insights on brain activity from measurements, EIT and EEG relies on detailed knowledge of the underlying electrical properties of the body. This is obtained from numerical models of the electric �field distribution therein. The inhomogeneous and anisotropic electric properties of human tissues make accurate modeling and simulation very challenging, leading to a tradeo�ff between physical accuracy and technical feasibility, which currently severely limits the capabilities of these techniques. Moreover elaboration of data recorded requires usage of regularization techniques computationally intensive, which influences the application with heavy temporal constraints (such as BCI). This work focuses on the parallel implementation of a work-flow for EEG and EIT data processing. The resulting software is accelerated using multi-core GPUs, in order to provide solution in reasonable times and address requirements of real-time BCI systems, without over-simplifying the complexity and accuracy of the head models.