Cooperative communication and distributed detection in wireless sensor networks

Recent progress in microelectronic and wireless communications have enabled the development of low cost, low power, multifunctional sensors, which has allowed the birth of new type of networks named wireless sensor networks (WSNs). The main features of such networks are: the nodes can be positioned randomly over a given field with a high density; each node operates both like sensor (for collection of environmental data) as well as transceiver (for transmission of information to the data retrieval); the nodes have limited energy resources. The use of wireless communications and the small size of nodes, make this type of networks suitable for a large number of applications. For example, sensor nodes can be used to monitor a high risk region, as near a volcano; in a hospital they could be used to monitor physical conditions of patients. For each of these possible application scenarios, it is necessary to guarantee a trade-off between energy consumptions and communication reliability. The thesis investigates the use of WSNs in two possible scenarios and for each of them suggests a solution that permits to solve relating problems considering the trade-off introduced. The first scenario considers a network with a high number of nodes deployed in a given geographical area without detailed planning that have to transmit data toward a coordinator node, named sink, that we assume to be located onboard an unmanned aerial vehicle (UAV). This is a practical example of reachback communication, characterized by the high density of nodes that have to transmit data reliably and efficiently towards a far receiver. It is considered that each node transmits a common shared message directly to the receiver onboard the UAV whenever it receives a broadcast message (triggered for example by the vehicle). We assume that the communication channels between the local nodes and the receiver are subject to fading and noise. The receiver onboard the UAV must be able to fuse the weak and noisy signals in a coherent way to receive the data reliably. It is proposed a cooperative diversity concept as an effective solution to the reachback problem. In particular, it is considered a spread spectrum (SS) transmission scheme in conjunction with a fusion center that can exploit cooperative diversity, without requiring stringent synchronization between nodes. The idea consists of simultaneous transmission of the common message among the nodes and a Rake reception at the fusion center. The proposed solution is mainly motivated by two goals: the necessity to have simple nodes (to this aim we move the computational complexity to the receiver onboard the UAV), and the importance to guarantee high levels of energy efficiency of the network, thus increasing the network lifetime. The proposed scheme is analyzed in order to better understand the effectiveness of the approach presented. The performance metrics considered are both the theoretical limit on the maximum amount of data that can be collected by the receiver, as well as the error probability with a given modulation scheme. Since we deal with a WSN, both of these performance are evaluated taking into consideration the energy efficiency of the network. The second scenario considers the use of a chain network for the detection of fires by using nodes that have a double function of sensors and routers. The first one is relative to the monitoring of a temperature parameter that allows to take a local binary decision of target (fire) absent/present. The second one considers that each node receives a decision made by the previous node of the chain, compares this with that deriving by the observation of the phenomenon, and transmits the final result to the next node. The chain ends at the sink node that transmits the received decision to the user. In this network the goals are to limit throughput in each sensor-to-sensor link and minimize probability of error at the last stage of the chain. This is a typical scenario of distributed detection. To obtain good performance it is necessary to define some fusion rules for each node to summarize local observations and decisions of the previous nodes, to get a final decision that it is transmitted to the next node. WSNs have been studied also under a practical point of view, describing both the main characteristics of IEEE802:15:4 standard and two commercial WSN platforms. By using a commercial WSN platform it is realized an agricultural application that has been tested in a six months on-field experimentation.

Isospin dependence of the Ar+Ni fusion-evaporation cross section

The fundamental goal of this thesis is the determination of the isospin dependence of the Ar+Ni fusion-evaporation cross section. Three Ar isotope beams, with energies of about 13AMeV, have been accelerated and impinged onto isotopically enriched Ni targets, in order to produce Pd nuclei, with mass number varying from 92 to 104. The measurements have been performed by the high performance 4pi detector INDRA, coupled with the magnetic spectrometer VAMOS. Even if the results are very preliminary, the obtained fusion-evaporation cross sections behaviour gives a hint at the possible isospin dependence of the fusion-evaporation cross sections.

3D probability tomography: theoretical developments and applications to high-resolution geophysical prospecting

The theory of the 3D multipole probability tomography method (3D GPT) to image source poles, dipoles, quadrupoles and octopoles, of a geophysical vector or scalar field dataset is developed. A geophysical dataset is assumed to be the response of an aggregation of poles, dipoles, quadrupoles and octopoles. These physical sources are used to reconstruct without a priori assumptions the most probable position and shape of the true geophysical buried sources, by determining the location of their centres and critical points of their boundaries, as corners, wedges and vertices. This theory, then, is adapted to the geoelectrical, gravity and self potential methods. A few synthetic examples using simple geometries and three field examples are discussed in order to demonstrate the notably enhanced resolution power of the new approach. At first, the application to a field example related to a dipole–dipole geoelectrical survey carried out in the archaeological park of Pompei is presented. The survey was finalised to recognize remains of the ancient Roman urban network including roads, squares and buildings, which were buried under the thick pyroclastic cover fallen during the 79 AD Vesuvius eruption. The revealed anomaly structures are ascribed to wellpreserved remnants of some aligned walls of Roman edifices, buried and partially destroyed by the 79 AD Vesuvius pyroclastic fall. Then, a field example related to a gravity survey carried out in the volcanic area of Mount Etna (Sicily, Italy) is presented, aimed at imaging as accurately as possible the differential mass density structure within the first few km of depth inside the volcanic apparatus. An assemblage of vertical prismatic blocks appears to be the most probable gravity model of the Etna apparatus within the first 5 km of depth below sea level. Finally, an experimental SP dataset collected in the Mt. Somma-Vesuvius volcanic district (Naples, Italy) is elaborated in order to define location and shape of the sources of two SP anomalies of opposite sign detected in the northwestern sector of the surveyed area. The modelled sources are interpreted as the polarization state induced by an intense hydrothermal convective flow mechanism within the volcanic apparatus, from the free surface down to about 3 km of depth b.s.l..

Dynamic stabilization of Rayleigh-Taylor instability of ablation fronts in inertial confinement fusion

One of the most important problems in inertial confinement fusion is how to find a way to mitigate the onset of the Rayleigh-Taylor instability which arises in the ablation front during the compression. In this thesis it is studied in detail the possibility of using for such a purpose the well-known mechanism of dynamic stabilization, already applied to other dynamical systems such as the inverted pendulum. In this context, a periodic acceleration superposed to the background gravity generates a vertical vibration of the ablation front itself. The effects of different driving modulations (Dirac deltas and square waves) are analyzed from a theoretical point of view, with a focus on stabilization of ion beam driven ablation fronts, and a comparison is made, in order to look for optimization.

Towards a Reconstruction of Thermal Properties of Light Nuclei from Fusion - Evaporation reactions

This thesis work has been developed in the framework of a new experimental campaign, proposed by the NUCL-EX Collaboration (INFN III Group), in order to progress in the understanding of the statistical properties of light nuclei, at excitation energies above particle emission threshold, by measuring exclusive data from fusion-evaporation reactions. The determination of the nuclear level density in the A~20 region, the understanding of the statistical behavior of light nuclei with excitation energies ~3 A.MeV, and the measurement of observables linked to the presence of cluster structures of nuclear excited levels are the main physics goals of this work. On the theory side, the contribution to this project given by this work lies in the development of a dedicated Monte-Carlo Hauser-Feshbach code for the evaporation of the compound nucleus. The experimental part of this thesis has consisted in the participation to the measurement 12C+12C at 95 MeV beam energy, at Laboratori Nazionali di Legnaro - INFN, using the GARFIELD+Ring Counter(RCo) set-up, from the beam-time request to the data taking, data reduction, detector calibrations and data analysis. Different results of the data analysis are presented in this thesis, together with a theoretical study of the system, performed with the new statistical decay code. As a result of this work, constraints on the nuclear level density at high excitation energy for light systems ranging from C up to Mg are given. Moreover, pre-equilibrium effects, tentatively interpreted as alpha-clustering effects, are put in evidence, both in the entrance channel of the reaction and in the dissipative dynamics on the path towards thermalisation.

A novel specific genetic translocation in epithelioid hemangioendotelioma, showing a fusion of the WWTR1 and CAMTA1 genes, supports the monoclonality of multifocal epithelioid hemangioendotelioma.

Like other vascular tumors, epithelioid hemangioendothelioma (EHE) is multifocal in approximately 50% of cases, and it is unclear whether the separate lesions represent multifocal disease or metastases. We hypothesized that the identification of an identical WWTR1-CAMTA1 rearrangement in different EHEs from the same patient supports the monoclonal origin of EHE. To test our hypothesis, we undertook a molecular analysis of two multicentric EHEs of the liver, including separate tumor samples from each patient. Matherial and Methods: We retrieved two cases of EHE with available tissue for molecular analysis. In both cases, fluorescence in situ hybridization (FISH) was performed to identify the presence of the WWTR1-CAMTA1 rearrangement to confirm the histologic diagnosis of EHE, as previously described. The reverse transcription-polymerase chain reaction (RT-PCR) products were analyzed by electrophoresis and the RT-PCR–amplified products were sequenced using the Sanger method. Results: FISH analysis revealed signal abnormalities in both WWTR1 and CAMTA1. Combined results confirmed the presence of the t(1;3)(1p36.23;3q25.1) translocation in both cases of EHE. Using RT-PCR analysis, we found that the size of the rearranged bands was identical in the different tumors from each patient. The sequence of the fusion gene confirmed a different WWTR1-CAMTA1 rearrangement in each patient, but an identical WWTR1-CAMTA1 rearrangement in the different lesions from each patient. Discussion: Because of its generally indolent clinical course, EHE is commonly classified as a multifocal, rather than metastatic, disease. In this study, we examined two cases of multifocal liver EHE and found an identical WWTR1-CAMTA1 rearrangement in each lesion from the same patient, but not between the two patients. These findings suggest that multifocal EHE arises from metastasis of the same neoplastic clone rather than from the simultaneous formation of multiple neoplastic clones, which supports the monoclonal origin of multifocal EHE.

Bayesian space-time data fusion for real-time forecasting and map uncertainty

Environmental computer models are deterministic models devoted to predict several environmental phenomena such as air pollution or meteorological events. Numerical model output is given in terms of averages over grid cells, usually at high spatial and temporal resolution. However, these outputs are often biased with unknown calibration and not equipped with any information about the associated uncertainty. Conversely, data collected at monitoring stations is more accurate since they essentially provide the true levels. Due the leading role played by numerical models, it now important to compare model output with observations. Statistical methods developed to combine numerical model output and station data are usually referred to as data fusion. In this work, we first combine ozone monitoring data with ozone predictions from the Eta-CMAQ air quality model in order to forecast real-time current 8-hour average ozone level defined as the average of the previous four hours, current hour, and predictions for the next three hours. We propose a Bayesian downscaler model based on first differences with a flexible coefficient structure and an efficient computational strategy to fit model parameters. Model validation for the eastern United States shows consequential improvement of our fully inferential approach compared with the current real-time forecasting system. Furthermore, we consider the introduction of temperature data from a weather forecast model into the downscaler, showing improved real-time ozone predictions. Finally, we introduce a hierarchical model to obtain spatially varying uncertainty associated with numerical model output. We show how we can learn about such uncertainty through suitable stochastic data fusion modeling using some external validation data. We illustrate our Bayesian model by providing the uncertainty map associated with a temperature output over the northeastern United States.