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.

The meccano method for isogeometric solid modeling and applications

[EN]We present a new method to construct a trivariate T-spline representation of complex solids for the application of isogeometric analysis. We take a genus-zero solid as a basis of our study, but at the end of the work we explain the way to generalize the results to any genus solids. The proposed technique only demands a surface triangulation of the solid as input data. The key of this method lies in obtaining a volumetric parameterization between the solid and the parametric domain, the unitary cube. To do that, an adaptive tetrahedral mesh of the parametric domain is isomorphically transformed onto the solid by applying a mesh untangling and smoothing procedure...

Computational methods for the analysis of protein structure and function

The vast majority of known proteins have not yet been experimentally characterized and little is known about their function. The design and implementation of computational tools can provide insight into the function of proteins based on their sequence, their structure, their evolutionary history and their association with other proteins. Knowledge of the three-dimensional (3D) structure of a protein can lead to a deep understanding of its mode of action and interaction, but currently the structures of <1% of sequences have been experimentally solved. For this reason, it became urgent to develop new methods that are able to computationally extract relevant information from protein sequence and structure. The starting point of my work has been the study of the properties of contacts between protein residues, since they constrain protein folding and characterize different protein structures. Prediction of residue contacts in proteins is an interesting problem whose solution may be useful in protein folding recognition and de novo design. The prediction of these contacts requires the study of the protein inter-residue distances related to the specific type of amino acid pair that are encoded in the so-called contact map. An interesting new way of analyzing those structures came out when network studies were introduced, with pivotal papers demonstrating that protein contact networks also exhibit small-world behavior. In order to highlight constraints for the prediction of protein contact maps and for applications in the field of protein structure prediction and/or reconstruction from experimentally determined contact maps, I studied to which extent the characteristic path length and clustering coefficient of the protein contacts network are values that reveal characteristic features of protein contact maps. Provided that residue contacts are known for a protein sequence, the major features of its 3D structure could be deduced by combining this knowledge with correctly predicted motifs of secondary structure. In the second part of my work I focused on a particular protein structural motif, the coiled-coil, known to mediate a variety of fundamental biological interactions. Coiled-coils are found in a variety of structural forms and in a wide range of proteins including, for example, small units such as leucine zippers that drive the dimerization of many transcription factors or more complex structures such as the family of viral proteins responsible for virus-host membrane fusion. The coiled-coil structural motif is estimated to account for 5-10% of the protein sequences in the various genomes. Given their biological importance, in my work I introduced a Hidden Markov Model (HMM) that exploits the evolutionary information derived from multiple sequence alignments, to predict coiled-coil regions and to discriminate coiled-coil sequences. The results indicate that the new HMM outperforms all the existing programs and can be adopted for the coiled-coil prediction and for large-scale genome annotation. Genome annotation is a key issue in modern computational biology, being the starting point towards the understanding of the complex processes involved in biological networks. The rapid growth in the number of protein sequences and structures available poses new fundamental problems that still deserve an interpretation. Nevertheless, these data are at the basis of the design of new strategies for tackling problems such as the prediction of protein structure and function. Experimental determination of the functions of all these proteins would be a hugely time-consuming and costly task and, in most instances, has not been carried out. As an example, currently, approximately only 20% of annotated proteins in the Homo sapiens genome have been experimentally characterized. A commonly adopted procedure for annotating protein sequences relies on the "inheritance through homology" based on the notion that similar sequences share similar functions and structures. This procedure consists in the assignment of sequences to a specific group of functionally related sequences which had been grouped through clustering techniques. The clustering procedure is based on suitable similarity rules, since predicting protein structure and function from sequence largely depends on the value of sequence identity. However, additional levels of complexity are due to multi-domain proteins, to proteins that share common domains but that do not necessarily share the same function, to the finding that different combinations of shared domains can lead to different biological roles. In the last part of this study I developed and validate a system that contributes to sequence annotation by taking advantage of a validated transfer through inheritance procedure of the molecular functions and of the structural templates. After a cross-genome comparison with the BLAST program, clusters were built on the basis of two stringent constraints on sequence identity and coverage of the alignment. The adopted measure explicity answers to the problem of multi-domain proteins annotation and allows a fine grain division of the whole set of proteomes used, that ensures cluster homogeneity in terms of sequence length. A high level of coverage of structure templates on the length of protein sequences within clusters ensures that multi-domain proteins when present can be templates for sequences of similar length. This annotation procedure includes the possibility of reliably transferring statistically validated functions and structures to sequences considering information available in the present data bases of molecular functions and structures.

Two fluid numerical model for coastal applications

Wave breaking is an important coastal process, influencing hydro-morphodynamic processes such as turbulence generation and wave energy dissipation, run-up on the beach and overtopping of coastal defence structures. During breaking, waves are complex mixtures of air and water (“white water”) whose properties affect velocity and pressure fields in the vicinity of the free surface and, depending on the breaker characteristics, different mechanisms for air entrainment are usually observed. Several laboratory experiments have been performed to investigate the role of air bubbles in the wave breaking process (Chanson & Cummings, 1994, among others) and in wave loading on vertical wall (Oumeraci et al., 2001; Peregrine et al., 2006, among others), showing that the air phase is not negligible since the turbulent energy dissipation involves air-water mixture. The recent advancement of numerical models has given valuable insights in the knowledge of wave transformation and interaction with coastal structures. Among these models, some solve the RANS equations coupled with a free-surface tracking algorithm and describe velocity, pressure, turbulence and vorticity fields (Lara et al. 2006 a-b, Clementi et al., 2007). The single-phase numerical model, in which the constitutive equations are solved only for the liquid phase, neglects effects induced by air movement and trapped air bubbles in water. Numerical approximations at the free surface may induce errors in predicting breaking point and wave height and moreover, entrapped air bubbles and water splash in air are not properly represented. The aim of the present thesis is to develop a new two-phase model called COBRAS2 (stands for Cornell Breaking waves And Structures 2 phases), that is the enhancement of the single-phase code COBRAS0, originally developed at Cornell University (Lin & Liu, 1998). In the first part of the work, both fluids are considered as incompressible, while the second part will treat air compressibility modelling. The mathematical formulation and the numerical resolution of the governing equations of COBRAS2 are derived and some model-experiment comparisons are shown. In particular, validation tests are performed in order to prove model stability and accuracy. The simulation of the rising of a large air bubble in an otherwise quiescent water pool reveals the model capability to reproduce the process physics in a realistic way. Analytical solutions for stationary and internal waves are compared with corresponding numerical results, in order to test processes involving wide range of density difference. Waves induced by dam-break in different scenarios (on dry and wet beds, as well as on a ramp) are studied, focusing on the role of air as the medium in which the water wave propagates and on the numerical representation of bubble dynamics. Simulations of solitary and regular waves, characterized by both spilling and plunging breakers, are analyzed with comparisons with experimental data and other numerical model in order to investigate air influence on wave breaking mechanisms and underline model capability and accuracy. Finally, modelling of air compressibility is included in the new developed model and is validated, revealing an accurate reproduction of processes. Some preliminary tests on wave impact on vertical walls are performed: since air flow modelling allows to have a more realistic reproduction of breaking wave propagation, the dependence of wave breaker shapes and aeration characteristics on impact pressure values is studied and, on the basis of a qualitative comparison with experimental observations, the numerical simulations achieve good results.

Kernel Methods for Tree Structured Data

Machine learning comprises a series of techniques for automatic extraction of meaningful information from large collections of noisy data. In many real world applications, data is naturally represented in structured form. Since traditional methods in machine learning deal with vectorial information, they require an a priori form of preprocessing. Among all the learning techniques for dealing with structured data, kernel methods are recognized to have a strong theoretical background and to be effective approaches. They do not require an explicit vectorial representation of the data in terms of features, but rely on a measure of similarity between any pair of objects of a domain, the kernel function. Designing fast and good kernel functions is a challenging problem. In the case of tree structured data two issues become relevant: kernel for trees should not be sparse and should be fast to compute. The sparsity problem arises when, given a dataset and a kernel function, most structures of the dataset are completely dissimilar to one another. In those cases the classifier has too few information for making correct predictions on unseen data. In fact, it tends to produce a discriminating function behaving as the nearest neighbour rule. Sparsity is likely to arise for some standard tree kernel functions, such as the subtree and subset tree kernel, when they are applied to datasets with node labels belonging to a large domain. A second drawback of using tree kernels is the time complexity required both in learning and classification phases. Such a complexity can sometimes prevents the kernel application in scenarios involving large amount of data. This thesis proposes three contributions for resolving the above issues of kernel for trees. A first contribution aims at creating kernel functions which adapt to the statistical properties of the dataset, thus reducing its sparsity with respect to traditional tree kernel functions. Specifically, we propose to encode the input trees by an algorithm able to project the data onto a lower dimensional space with the property that similar structures are mapped similarly. By building kernel functions on the lower dimensional representation, we are able to perform inexact matchings between different inputs in the original space. A second contribution is the proposal of a novel kernel function based on the convolution kernel framework. Convolution kernel measures the similarity of two objects in terms of the similarities of their subparts. Most convolution kernels are based on counting the number of shared substructures, partially discarding information about their position in the original structure. The kernel function we propose is, instead, especially focused on this aspect. A third contribution is devoted at reducing the computational burden related to the calculation of a kernel function between a tree and a forest of trees, which is a typical operation in the classification phase and, for some algorithms, also in the learning phase. We propose a general methodology applicable to convolution kernels. Moreover, we show an instantiation of our technique when kernels such as the subtree and subset tree kernels are employed. In those cases, Direct Acyclic Graphs can be used to compactly represent shared substructures in different trees, thus reducing the computational burden and storage requirements.

Design of optical networks for advanced applications

Nowadays, computing is migrating from traditional high performance and distributed computing to pervasive and utility computing based on heterogeneous networks and clients. The current trend suggests that future IT services will rely on distributed resources and on fast communication of heterogeneous contents. The success of this new range of services is directly linked to the effectiveness of the infrastructure in delivering them. The communication infrastructure will be the aggregation of different technologies even though the current trend suggests the emergence of single IP based transport service. Optical networking is a key technology to answer the increasing requests for dynamic bandwidth allocation and configure multiple topologies over the same physical layer infrastructure, optical networks today are still “far” from accessible from directly configure and offer network services and need to be enriched with more “user oriented” functionalities. However, current Control Plane architectures only facilitate efficient end-to-end connectivity provisioning and certainly cannot meet future network service requirements, e.g. the coordinated control of resources. The overall objective of this work is to provide the network with the improved usability and accessibility of the services provided by the Optical Network. More precisely, the definition of a service-oriented architecture is the enable technology to allow user applications to gain benefit of advanced services over an underlying dynamic optical layer. The definition of a service oriented networking architecture based on advanced optical network technologies facilitates users and applications access to abstracted levels of information regarding offered advanced network services. This thesis faces the problem to define a Service Oriented Architecture and its relevant building blocks, protocols and languages. In particular, this work has been focused on the use of the SIP protocol as a inter-layers signalling protocol which defines the Session Plane in conjunction with the Network Resource Description language. On the other hand, an advantage optical network must accommodate high data bandwidth with different granularities. Currently, two main technologies are emerging promoting the development of the future optical transport network, Optical Burst and Packet Switching. Both technologies respectively promise to provide all optical burst or packet switching instead of the current circuit switching. However, the electronic domain is still present in the scheduler forwarding and routing decision. Because of the high optics transmission frequency the burst or packet scheduler faces a difficult challenge, consequentially, high performance and time focused design of both memory and forwarding logic is need. This open issue has been faced in this thesis proposing an high efficiently implementation of burst and packet scheduler. The main novelty of the proposed implementation is that the scheduling problem has turned into simple calculation of a min/max function and the function complexity is almost independent of on the traffic conditions.

NMR, Metabonomics and Molecular Profiles: Applications to the Quality Assessment of Foodstuffs

Nuclear Magnetic Resonance (NMR) is a branch of spectroscopy that is based on the fact that many atomic nuclei may be oriented by a strong magnetic field and will absorb radiofrequency radiation at characteristic frequencies. The parameters that can be measured on the resulting spectral lines (line positions, intensities, line widths, multiplicities and transients in time-dependent experi-ments) can be interpreted in terms of molecular structure, conformation, molecular motion and other rate processes. In this way, high resolution (HR) NMR allows performing qualitative and quantitative analysis of samples in solution, in order to determine the structure of molecules in solution and not only. In the past, high-field NMR spectroscopy has mainly concerned with the elucidation of chemical structure in solution, but today is emerging as a powerful exploratory tool for probing biochemical and physical processes. It represents a versatile tool for the analysis of foods. In literature many NMR studies have been reported on different type of food such as wine, olive oil, coffee, fruit juices, milk, meat, egg, starch granules, flour, etc using different NMR techniques. Traditionally, univariate analytical methods have been used to ex-plore spectroscopic data. This method is useful to measure or to se-lect a single descriptive variable from the whole spectrum and , at the end, only this variable is analyzed. This univariate methods ap-proach, applied to HR-NMR data, lead to different problems due especially to the complexity of an NMR spectrum. In fact, the lat-ter is composed of different signals belonging to different mole-cules, but it is also true that the same molecules can be represented by different signals, generally strongly correlated. The univariate methods, in this case, takes in account only one or a few variables, causing a loss of information. Thus, when dealing with complex samples like foodstuff, univariate analysis of spectra data results not enough powerful. Spectra need to be considered in their wholeness and, for analysing them, it must be taken in consideration the whole data matrix: chemometric methods are designed to treat such multivariate data. Multivariate data analysis is used for a number of distinct, differ-ent purposes and the aims can be divided into three main groups: • data description (explorative data structure modelling of any ge-neric n-dimensional data matrix, PCA for example); • regression and prediction (PLS); • classification and prediction of class belongings for new samples (LDA and PLS-DA and ECVA). The aim of this PhD thesis was to verify the possibility of identify-ing and classifying plants or foodstuffs, in different classes, based on the concerted variation in metabolite levels, detected by NMR spectra and using the multivariate data analysis as a tool to inter-pret NMR information. It is important to underline that the results obtained are useful to point out the metabolic consequences of a specific modification on foodstuffs, avoiding the use of a targeted analysis for the different metabolites. The data analysis is performed by applying chemomet-ric multivariate techniques to the NMR dataset of spectra acquired. The research work presented in this thesis is the result of a three years PhD study. This thesis reports the main results obtained from these two main activities: A1) Evaluation of a data pre-processing system in order to mini-mize unwanted sources of variations, due to different instrumental set up, manual spectra processing and to sample preparations arte-facts; A2) Application of multivariate chemiometric models in data analy-sis.

Applications of High Speed Configurable Logic Devices in Modern Particle Physics Experiments

Several activities were conducted during my PhD activity. For the NEMO experiment a collaboration between the INFN/University groups of Catania and Bologna led to the development and production of a mixed signal acquisition board for the Nemo Km3 telescope. The research concerned the feasibility study for a different acquisition technique quite far from that adopted in the NEMO Phase 1 telescope. The DAQ board that we realized exploits the LIRA06 front-end chip for the analog acquisition of anodic an dynodic sources of a PMT (Photo-Multiplier Tube). The low-power analog acquisition allows to sample contemporaneously multiple channels of the PMT at different gain factors in order to increase the signal response linearity over a wider dynamic range. Also the auto triggering and self-event-classification features help to improve the acquisition performance and the knowledge on the neutrino event. A fully functional interface towards the first level data concentrator, the Floor Control Module, has been integrated as well on the board, and a specific firmware has been realized to comply with the present communication protocols. This stage of the project foresees the use of an FPGA, a high speed configurable device, to provide the board with a flexible digital logic control core. After the validation of the whole front-end architecture this feature would be probably integrated in a common mixed-signal ASIC (Application Specific Integrated Circuit). The volatile nature of the configuration memory of the FPGA implied the integration of a flash ISP (In System Programming) memory and a smart architecture for a safe remote reconfiguration of it. All the integrated features of the board have been tested. At the Catania laboratory the behavior of the LIRA chip has been investigated in the digital environment of the DAQ board and we succeeded in driving the acquisition with the FPGA. The PMT pulses generated with an arbitrary waveform generator were correctly triggered and acquired by the analog chip, and successively they were digitized by the on board ADC under the supervision of the FPGA. For the communication towards the data concentrator a test bench has been realized in Bologna where, thanks to a lending of the Roma University and INFN, a full readout chain equivalent to that present in the NEMO phase-1 was installed. These tests showed a good behavior of the digital electronic that was able to receive and to execute command imparted by the PC console and to answer back with a reply. The remotely configurable logic behaved well too and demonstrated, at least in principle, the validity of this technique. A new prototype board is now under development at the Catania laboratory as an evolution of the one described above. This board is going to be deployed within the NEMO Phase-2 tower in one of its floors dedicated to new front-end proposals. This board will integrate a new analog acquisition chip called SAS (Smart Auto-triggering Sampler) introducing thus a new analog front-end but inheriting most of the digital logic present in the current DAQ board discussed in this thesis. For what concern the activity on high-resolution vertex detectors, I worked within the SLIM5 collaboration for the characterization of a MAPS (Monolithic Active Pixel Sensor) device called APSEL-4D. The mentioned chip is a matrix of 4096 active pixel sensors with deep N-well implantations meant for charge collection and to shield the analog electronics from digital noise. The chip integrates the full-custom sensors matrix and the sparsifification/readout logic realized with standard-cells in STM CMOS technology 130 nm. For the chip characterization a test-beam has been set up on the 12 GeV PS (Proton Synchrotron) line facility at CERN of Geneva (CH). The collaboration prepared a silicon strip telescope and a DAQ system (hardware and software) for data acquisition and control of the telescope that allowed to store about 90 million events in 7 equivalent days of live-time of the beam. My activities concerned basically the realization of a firmware interface towards and from the MAPS chip in order to integrate it on the general DAQ system. Thereafter I worked on the DAQ software to implement on it a proper Slow Control interface of the APSEL4D. Several APSEL4D chips with different thinning have been tested during the test beam. Those with 100 and 300 um presented an overall efficiency of about 90% imparting a threshold of 450 electrons. The test-beam allowed to estimate also the resolution of the pixel sensor providing good results consistent with the pitch/sqrt(12) formula. The MAPS intrinsic resolution has been extracted from the width of the residual plot taking into account the multiple scattering effect.

Advanced applications of X-ray Absorption Spectroscopy to the study of protein metal sites.

We present a study of the metal sites of different proteins through X-ray Absorption Fine Structure (XAFS) spectroscopy. First of all, the capabilities of XAFS analysis have been improved by ab initio simulation of the near-edge region of the spectra, and an original analysis method has been proposed. The method subsequently served ad a tool to treat diverse biophysical problems, like the inhibition of proton-translocating proteins by metal ions and the matrix effect exerted on photosynthetic proteins (the bacterial Reaction Center, RC) by strongly dehydrate sugar matrices. A time-resolved study of Fe site of RC with μs resolution has been as well attempted. Finally, a further step aimed to improve the reliability of XAFS analysis has been performed by calculating the dynamical parameters of the metal binding cluster by means of DFT methods, and the theoretical result obtained for MbCO has been successfully compared with experimental data.

Cluster-derived Gold/Iron catalysts for environmental applications

During the last years we assisted to an exponential growth of scientific discoveries for catalysis by gold and many applications have been found for Au-based catalysts. In the literature there are several studies concerning the use of gold-based catalysts for environmental applications and good results are reported for the catalytic combustion of different volatile organic compounds (VOCs). Recently it has also been established that gold-based catalysts are potentially capable of being effectively employed in fuel cells in order to remove CO traces by preferential CO oxidation in H2-rich streams. Bi-metallic catalysts have attracted increasing attention because of their markedly different properties from either of the costituent metals, and above all their enhanced catalytic activity, selectivity and stability. In the literature there are several studies demostrating the beneficial effect due to the addition of an iron component to gold supported catalysts in terms of enhanced activity, selectivity, resistence to deactivation and prolonged lifetime of the catalyst. In this work we tried to develop a methodology for the preparation of iron stabilized gold nanoparticles with controlled size and composition, particularly in terms of obtaining an intimate contact between different phases, since it is well known that the catalytic behaviour of multi-component supported catalysts is strongly influenced by the size of the metal particles and by their reciprocal interaction. Ligand stabilized metal clusters, with nanometric dimensions, are possible precursors for the preparation of catalytically active nanoparticles with controlled dimensions and compositions. Among these, metal carbonyl clusters are quite attractive, since they can be prepared with several different sizes and compositions and, moreover, they are decomposed under very mild conditions. A novel preparation method was developed during this thesis for the preparation of iron and gold/iron supported catalysts using bi-metallic carbonyl clusters as precursors of highly dispersed nanoparticles over TiO2 and CeO2, which are widely considered two of the most suitable supports for gold nanoparticles. Au/FeOx catalysts were prepared by employing the bi-metallic carbonyl cluster salts [NEt4]4[Au4Fe4(CO)16] (Fe/Au=1) and [NEt4][AuFe4(CO)16] (Fe/Au=4), and for comparison FeOx samples were prepared by employing the homometallic [NEt4][HFe3(CO)11] cluster. These clusters were prepared by Prof. Longoni research group (Department of Physical and Inorganic Chemistry- University of Bologna). Particular attention was dedicated to the optimization of a suitable thermal treatment in order to achieve, apart from a good Au and Fe metal dispersion, also the formation of appropriate species with good catalytic properties. A deep IR study was carried out in order to understand the physical interaction between clusters and different supports and detect the occurrence of chemical reactions between them at any stage of the preparation. The characterization by BET, XRD, TEM, H2-TPR, ICP-AES and XPS was performed in order to investigate the catalysts properties, whit particular attention to the interaction between Au and Fe and its influence on the catalytic activity. This novel preparation method resulted in small gold metallic nanoparticles surrounded by highly dispersed iron oxide species, essentially in an amorphous phase, on both TiO2 and CeO2. The results presented in this thesis confirmed that FeOx species can stabilize small Au particles, since keeping costant the gold content but introducing a higher iron amount a higher metal dispersion was achieved. Partial encapsulation of gold atoms by iron species was observed since the Au/Fe surface ratio was found much lower than bulk ratio and a strong interaction between gold and oxide species, both of iron oxide and supports, was achieved. The prepared catalysts were tested in the total oxidation of VOCs, using toluene and methanol as probe molecules for aromatics and alchols, respectively, and in the PROX reaction. Different performances were observed on titania and ceria catalysts, on both toluene and methanol combustion. Toluene combustion on titania catalyst was found to be enhanced increasing iron loading while a moderate effect on FeOx-Ti activity was achieved by Au addition. In this case toluene combustion was improved due to a higher oxygen mobility depending on enhanced oxygen activation by FeOx and Au/FeOx dispersed on titania. On the contrary ceria activity was strongly decreased in the presence of FeOx, while the introduction of gold was found to moderate the detrimental effect of iron species. In fact, excellent ceria performances are due to its ability to adsorb toluene and O2. Since toluene activation is the determining factor for its oxidation, the partial coverage of ceria sites, responsible of toluene adsorption, by FeOx species finely dispersed on the surface resulted in worse efficiency in toluene combustion. Better results were obtained for both ceria and titania catalysts on methanol total oxidation. In this case, the performances achieved on differently supported catalysts indicate that the oxygen mobility is the determining factor in this reaction. The introduction of gold on both TiO2 and CeO2 catalysts, lead to a higher oxygen mobility due to the weakening of both Fe-O and Ce-O bonds and consequently to enhanced methanol combustion. The catalytic activity was found to strongly depend on oxygen mobility and followed the same trend observed for catalysts reducibility. Regarding CO PROX reaction, it was observed that Au/FeOx titania catalysts are less active than ceria ones, due to the lower reducibility of titania compared to ceria. In fact the availability of lattice oxygen involved in PROX reaction is much higher in the latter catalysts. However, the CO PROX performances observed for ceria catalysts are not really high compared to data reported in literature, probably due to the very low Au/Fe surface ratio achieved with this preparation method. CO preferential oxidation was found to strongly depend on Au particle size but also on surface oxygen reducibility, depending on the different oxide species which can be formed using different thermal treatment conditions or varying the iron loading over the support.

Use of solvents and environmental friendly materials for applications in Green Chemistry

The present Thesis studies three alternative solvent groups as sustainable replacement of traditional organic solvents. Some aspects of fluorinated solvents, supercritical fluids and ionic liquids, have been analysed with a critical approach and their effective “greenness” has been evaluated from the points of view of the synthesis, the properties and the applications. In particular, the attention has been put on the environmental and human health issues, evaluating the eco-toxicity, the toxicity and the persistence, to underline that applicability and sustainability are subjects with equal importance. The “green” features of fluorous solvents and supercritical fluids are almost well-established; in particular supercritical carbon dioxide (scCO2) is probably the “greenest” solvent among the alternative solvent systems developed in the last years, enabling to combine numerous advantages both from the point of view of industrial/technological applications and eco-compatibility. In the Thesis the analysis of these two classes of alternative solvents has been mainly focused on their applicability, rather than the evaluation of their environmental impact. Specifically they have been evaluated as alternative media for non-aqueous biocatalysis. For this purpose, the hydrophobic ion pairing (HIP), which allows solubilising enzymes in apolar solvents by an ion pairing between the protein and a surfactant, has been investigated as effective enzymatic derivatisation technique to improve the catalytic activity under homogeneous conditions in non conventional media. The results showed that the complex enzyme-surfactant was much more active both in fluorous solvents and in supercritical carbon dioxide than the native form of the enzyme. Ionic liquids, especially imidazolium salts, have been proposed some years ago as “fully green” alternative solvents; however this epithet does not take into account several “brown” aspects such as their synthesis from petro-chemical starting materials, their considerable eco-toxicity, toxicity and resistance to biodegradation, and the difficulty of clearly outline applications in which ionic liquids are really more advantageous than traditional solvents. For all of these reasons in this Thesis a critical analysis of ionic liquids has been focused on three main topics: i) alternative synthesis by introducing structural moieties which could reduce the toxicity of the most known liquid salts, and by using starting materials from renewable resources; ii) on the evaluation of their environmental impact through eco-toxicological tests (Daphnia magna and Vibrio fischeri acute toxicity tests, and algal growth inhibition), toxicity tests (MTT test, AChE inhibition and LDH release tests) and fate and rate of aerobic biodegradation in soil and water; iii) and on the demonstration of their effectiveness as reaction media in organo-catalysis and as extractive solvents in the recovery of vegetable oil from terrestrial and aquatic biomass. The results about eco-toxicity tests with Daphnia magna, Vibrio fischeri and algae, and toxicity assay using cultured cell lines, clearly indicate that the difference in toxicity between alkyl and oxygenated cations relies in differences of polarity, according to the general trend of decreasing toxicity by decreasing the lipophilicity. Independently by the biological approach in fact, all the results are in agreement, showing a lower toxicity for compounds with oxygenated lateral chains than for those having purely alkyl lateral chains. These findings indicate that an appropriate choice of cation and anion structures is important not only to design the IL with improved and suitable chemico-physical properties but also to obtain safer and eco-friendly ILs. Moreover there is a clear indication that the composition of the abiotic environment has to be taken into account when the toxicity of ILs in various biological test systems is analysed, because, for example, the data reported in the Thesis indicate a significant influence of salinity variations on algal toxicity. Aerobic biodegradation of four imidazolium ionic liquids, two alkylated and two oxygenated, in soil was evaluated for the first time. Alkyl ionic liquids were shown to be biodegradable over the 6 months test period, and in contrast no significant mineralisation was observed with oxygenated derivatives. A different result was observed in the aerobic biodegradation of alkylated and oxygenated pyridinium ionic liquids in water because all the ionic liquids were almost completely degraded after 10 days, independently by the number of oxygen in the lateral chain of the cation. The synthesis of new ionic liquids by using renewable feedstock as starting materials, has been developed through the synthesis of furan-based ion pairs from furfural. The new ammonium salts were synthesised in very good yields, good purity of the products and wide versatility, combining low melting points with high decomposition temperatures and reduced viscosities. Regarding the possible applications as surfactants and biocides, furan-based salts could be a valuable alternative to benzyltributylammonium salts and benzalkonium chloride that are produced from non-renewable resources. A new procedure for the allylation of ketones and aldehydes with tetraallyltin in ionic liquids was developed. The reaction afforded high yields both in sulfonate-containing ILs and in ILs without sulfonate upon addition of a small amount of sulfonic acid. The checked reaction resulted in peculiar chemoselectivity favouring aliphatic substrates towards aromatic ketones and good stereoselectivity in the allylation of levoglucosenone. Finally ILs-based systems could be easily and successfully recycled, making the described procedure environmentally benign. The potential role of switchable polarity solvents as a green technology for the extraction of vegetable oil from terrestrial and aquatic biomass has been investigated. The extraction efficiency of terrestrial biomass rich in triacylglycerols, as soy bean flakes and sunflower seeds, was comparable to those of traditional organic solvents, being the yield of vegetable oils recovery very similar. Switchable polarity solvents as been also exploited for the first time in the extraction of hydrocarbons from the microalga Botryococcus braunii, demonstrating the efficiency of the process for the extraction of both dried microalgal biomass and directly of the aqueous growth medium. The switchable polarity solvents exhibited better extraction efficiency than conventional solvents, both with dried and liquid samples. This is an important issue considering that the harvest and the dewatering of algal biomass have a large impact on overall costs and energy balance.

In situ real-time investigation of Organic Ultra-Thin-Film transistors: growth, electrical properties and biosensing applications

Organic electronics has grown enormously during the last decades driven by the encouraging results and the potentiality of these materials for allowing innovative applications, such as flexible-large-area displays, low-cost printable circuits, plastic solar cells and lab-on-a-chip devices. Moreover, their possible field of applications reaches from medicine, biotechnology, process control and environmental monitoring to defense and security requirements. However, a large number of questions regarding the mechanism of device operation remain unanswered. Along the most significant is the charge carrier transport in organic semiconductors, which is not yet well understood. Other example is the correlation between the morphology and the electrical response. Even if it is recognized that growth mode plays a crucial role into the performance of devices, it has not been exhaustively investigated. The main goal of this thesis was the finding of a correlation between growth modes, electrical properties and morphology in organic thin-film transistors (OTFTs). In order to study the thickness dependence of electrical performance in organic ultra-thin-film transistors, we have designed and developed a home-built experimental setup for performing real-time electrical monitoring and post-growth in situ electrical characterization techniques. We have grown pentacene TFTs under high vacuum conditions, varying systematically the deposition rate at a fixed room temperature. The drain source current IDS and the gate source current IGS were monitored in real-time; while a complete post-growth in situ electrical characterization was carried out. At the end, an ex situ morphological investigation was performed by using the atomic force microscope (AFM). In this work, we present the correlation for pentacene TFTs between growth conditions, Debye length and morphology (through the correlation length parameter). We have demonstrated that there is a layered charge carriers distribution, which is strongly dependent of the growth mode (i.e. rate deposition for a fixed temperature), leading to a variation of the conduction channel from 2 to 7 monolayers (MLs). We conciliate earlier reported results that were apparently contradictory. Our results made evident the necessity of reconsidering the concept of Debye length in a layered low-dimensional device. Additionally, we introduce by the first time a breakthrough technique. This technique makes evident the percolation of the first MLs on pentacene TFTs by monitoring the IGS in real-time, correlating morphological phenomena with the device electrical response. The present thesis is organized in the following five chapters. Chapter 1 makes an introduction to the organic electronics, illustrating the operation principle of TFTs. Chapter 2 presents the organic growth from theoretical and experimental points of view. The second part of this chapter presents the electrical characterization of OTFTs and the typical performance of pentacene devices is shown. In addition, we introduce a correcting technique for the reconstruction of measurements hampered by leakage current. In chapter 3, we describe in details the design and operation of our innovative home-built experimental setup for performing real-time and in situ electrical measurements. Some preliminary results and the breakthrough technique for correlating morphological and electrical changes are presented. Chapter 4 meets the most important results obtained in real-time and in situ conditions, which correlate growth conditions, electrical properties and morphology of pentacene TFTs. In chapter 5 we describe applicative experiments where the electrical performance of pentacene TFTs has been investigated in ambient conditions, in contact to water or aqueous solutions and, finally, in the detection of DNA concentration as label-free sensor, within the biosensing framework.

Sistemi riconfigurabili a basso consumo per applicazioni di monitoraggio distribuito

The term Ambient Intelligence (AmI) refers to a vision on the future of the information society where smart, electronic environment are sensitive and responsive to the presence of people and their activities (Context awareness). In an ambient intelligence world, devices work in concert to support people in carrying out their everyday life activities, tasks and rituals in an easy, natural way using information and intelligence that is hidden in the network connecting these devices. This promotes the creation of pervasive environments improving the quality of life of the occupants and enhancing the human experience. AmI stems from the convergence of three key technologies: ubiquitous computing, ubiquitous communication and natural interfaces. Ambient intelligent systems are heterogeneous and require an excellent cooperation between several hardware/software technologies and disciplines, including signal processing, networking and protocols, embedded systems, information management, and distributed algorithms. Since a large amount of fixed and mobile sensors embedded is deployed into the environment, the Wireless Sensor Networks is one of the most relevant enabling technologies for AmI. WSN are complex systems made up of a number of sensor nodes which can be deployed in a target area to sense physical phenomena and communicate with other nodes and base stations. These simple devices typically embed a low power computational unit (microcontrollers, FPGAs etc.), a wireless communication unit, one or more sensors and a some form of energy supply (either batteries or energy scavenger modules). WNS promises of revolutionizing the interactions between the real physical worlds and human beings. Low-cost, low-computational power, low energy consumption and small size are characteristics that must be taken into consideration when designing and dealing with WSNs. To fully exploit the potential of distributed sensing approaches, a set of challengesmust be addressed. Sensor nodes are inherently resource-constrained systems with very low power consumption and small size requirements which enables than to reduce the interference on the physical phenomena sensed and to allow easy and low-cost deployment. They have limited processing speed,storage capacity and communication bandwidth that must be efficiently used to increase the degree of local ”understanding” of the observed phenomena. A particular case of sensor nodes are video sensors. This topic holds strong interest for a wide range of contexts such as military, security, robotics and most recently consumer applications. Vision sensors are extremely effective for medium to long-range sensing because vision provides rich information to human operators. However, image sensors generate a huge amount of data, whichmust be heavily processed before it is transmitted due to the scarce bandwidth capability of radio interfaces. In particular, in video-surveillance, it has been shown that source-side compression is mandatory due to limited bandwidth and delay constraints. Moreover, there is an ample opportunity for performing higher-level processing functions, such as object recognition that has the potential to drastically reduce the required bandwidth (e.g. by transmitting compressed images only when something ‘interesting‘ is detected). The energy cost of image processing must however be carefully minimized. Imaging could play and plays an important role in sensing devices for ambient intelligence. Computer vision can for instance be used for recognising persons and objects and recognising behaviour such as illness and rioting. Having a wireless camera as a camera mote opens the way for distributed scene analysis. More eyes see more than one and a camera system that can observe a scene from multiple directions would be able to overcome occlusion problems and could describe objects in their true 3D appearance. In real-time, these approaches are a recently opened field of research. In this thesis we pay attention to the realities of hardware/software technologies and the design needed to realize systems for distributed monitoring, attempting to propose solutions on open issues and filling the gap between AmI scenarios and hardware reality. The physical implementation of an individual wireless node is constrained by three important metrics which are outlined below. Despite that the design of the sensor network and its sensor nodes is strictly application dependent, a number of constraints should almost always be considered. Among them: • Small form factor to reduce nodes intrusiveness. • Low power consumption to reduce battery size and to extend nodes lifetime. • Low cost for a widespread diffusion. These limitations typically result in the adoption of low power, low cost devices such as low powermicrocontrollers with few kilobytes of RAMand tenth of kilobytes of program memory with whomonly simple data processing algorithms can be implemented. However the overall computational power of the WNS can be very large since the network presents a high degree of parallelism that can be exploited through the adoption of ad-hoc techniques. Furthermore through the fusion of information from the dense mesh of sensors even complex phenomena can be monitored. In this dissertation we present our results in building several AmI applications suitable for a WSN implementation. The work can be divided into two main areas:Low Power Video Sensor Node and Video Processing Alghoritm and Multimodal Surveillance . Low Power Video Sensor Nodes and Video Processing Alghoritms In comparison to scalar sensors, such as temperature, pressure, humidity, velocity, and acceleration sensors, vision sensors generate much higher bandwidth data due to the two-dimensional nature of their pixel array. We have tackled all the constraints listed above and have proposed solutions to overcome the current WSNlimits for Video sensor node. We have designed and developed wireless video sensor nodes focusing on the small size and the flexibility of reuse in different applications. The video nodes target a different design point: the portability (on-board power supply, wireless communication), a scanty power budget (500mW),while still providing a prominent level of intelligence, namely sophisticated classification algorithmand high level of reconfigurability. We developed two different video sensor node: The device architecture of the first one is based on a low-cost low-power FPGA+microcontroller system-on-chip. The second one is based on ARM9 processor. Both systems designed within the above mentioned power envelope could operate in a continuous fashion with Li-Polymer battery pack and solar panel. Novel low power low cost video sensor nodes which, in contrast to sensors that just watch the world, are capable of comprehending the perceived information in order to interpret it locally, are presented. Featuring such intelligence, these nodes would be able to cope with such tasks as recognition of unattended bags in airports, persons carrying potentially dangerous objects, etc.,which normally require a human operator. Vision algorithms for object detection, acquisition like human detection with Support Vector Machine (SVM) classification and abandoned/removed object detection are implemented, described and illustrated on real world data. Multimodal surveillance: In several setup the use of wired video cameras may not be possible. For this reason building an energy efficient wireless vision network for monitoring and surveillance is one of the major efforts in the sensor network community. Energy efficiency for wireless smart camera networks is one of the major efforts in distributed monitoring and surveillance community. For this reason, building an energy efficient wireless vision network for monitoring and surveillance is one of the major efforts in the sensor network community. The Pyroelectric Infra-Red (PIR) sensors have been used to extend the lifetime of a solar-powered video sensor node by providing an energy level dependent trigger to the video camera and the wireless module. Such approach has shown to be able to extend node lifetime and possibly result in continuous operation of the node.Being low-cost, passive (thus low-power) and presenting a limited form factor, PIR sensors are well suited for WSN applications. Moreover techniques to have aggressive power management policies are essential for achieving long-termoperating on standalone distributed cameras needed to improve the power consumption. We have used an adaptive controller like Model Predictive Control (MPC) to help the system to improve the performances outperforming naive power management policies.

Statistical methods for analysis and processing of medical ultrasound: applications to segmentation and restoration

In this thesis two major topics inherent with medical ultrasound images are addressed: deconvolution and segmentation. In the first case a deconvolution algorithm is described allowing statistically consistent maximum a posteriori estimates of the tissue reflectivity to be restored. These estimates are proven to provide a reliable source of information for achieving an accurate characterization of biological tissues through the ultrasound echo. The second topic involves the definition of a semi automatic algorithm for myocardium segmentation in 2D echocardiographic images. The results show that the proposed method can reduce inter- and intra observer variability in myocardial contours delineation and is feasible and accurate even on clinical data.

Assembly and characterization of supramolecular architectures for biosensor applications

The research has included the efforts in designing, assembling and structurally and functionally characterizing supramolecular biofunctional architectures for optical biosensing applications. In the first part of the study, a class of interfaces based on the biotin-NeutrAvidin binding matrix for the quantitative control of enzyme surface coverage and activity was developed. Genetically modified ß-lactamase was chosen as a model enzyme and attached to five different types of NeutrAvidin-functionalized chip surfaces through a biotinylated spacer. All matrices are suitable for achieving a controlled enzyme surface density. Data obtained by SPR are in excellent agreement with those derived from optical waveguide measurements. Among the various protein-binding strategies investigated in this study, it was found that stiffness and order between alkanethiol-based SAMs and PEGylated surfaces are very important. Matrix D based on a Nb2O5 coating showed a satisfactory regeneration possibility. The surface-immobilized enzymes were found to be stable and sufficiently active enough for a catalytic activity assay. Many factors, such as the steric crowding effect of surface-attached enzymes, the electrostatic interaction between the negatively charged substrate (Nitrocefin) and the polycationic PLL-g-PEG/PEG-Biotin polymer, mass transport effect, and enzyme orientation, are shown to influence the kinetic parameters of catalytic analysis. Furthermore, a home-built Surface Plasmon Resonance Spectrometer of SPR and a commercial miniature Fiber Optic Absorbance Spectrometer (FOAS), served as a combination set-up for affinity and catalytic biosensor, respectively. The parallel measurements offer the opportunity of on-line activity detection of surface attached enzymes. The immobilized enzyme does not have to be in contact with the catalytic biosensor. The SPR chip can easily be cleaned and used for recycling. Additionally, with regard to the application of FOAS, the integrated SPR technique allows for the quantitative control of the surface density of the enzyme, which is highly relevant for the enzymatic activity. Finally, the miniaturized portable FOAS devices can easily be combined as an add-on device with many other in situ interfacial detection techniques, such as optical waveguide lightmode spectroscopy (OWLS), the quartz crystal microbalance (QCM) measurements, or impedance spectroscopy (IS). Surface plasmon field-enhanced fluorescence spectroscopy (SPFS) allows for an absolute determination of intrinsic rate constants describing the true parameters that control interfacial hybridization. Thus it also allows for a study of the difference of the surface coupling influences between OMCVD gold particles and planar metal films presented in the second part. The multilayer growth process was found to proceed similarly to the way it occurs on planar metal substrates. In contrast to planar bulk metal surfaces, metal colloids exhibit a narrow UV-vis absorption band. This absorption band is observed if the incident photon frequency is resonant with the collective oscillation of the conduction electrons and is known as the localized surface plasmon resonance (LSPR). LSPR excitation results in extremely large molar extinction coefficients, which are due to a combination of both absorption and scattering. When considering metal-enhanced fluorescence we expect the absorption to cause quenching and the scattering to cause enhancement. Our further study will focus on the developing of a detection platform with larger gold particles, which will display a dominant scattering component and enhance the fluorescence signal. Furthermore, the results of sequence-specific detection of DNA hybridization based on OMCVD gold particles provide an excellent application potential for this kind of cheap, simple, and mild preparation protocol applied in this gold fabrication method. In the final chapter, SPFS was used for the in-depth characterizations of the conformational changes of commercial carboxymethyl dextran (CMD) substrate induced by pH and ionic strength variations were studied using surface plasmon resonance spectroscopy. The pH response of CMD is due to the changes in the electrostatics of the system between its protonated and deprotonated forms, while the ionic strength response is attributed from the charge screening effect of the cations that shield the charge of the carboxyl groups and prevent an efficient electrostatic repulsion. Additional studies were performed using SPFS with the aim of fluorophore labeling the carboxymethyl groups. CMD matrices showed typical pH and ionic strength responses, such as high pH and low ionic strength swelling. Furthermore, the effects of the surface charge and the crosslink density of the CMD matrix on the extent of stimuli responses were investigated. The swelling/collapse ratio decreased with decreasing surface concentration of the carboxyl groups and increasing crosslink density. The study of the CMD responses to external and internal variables will provide valuable background information for practical applications.