Progetto di reti Sensori Wireless e tecniche di Fusione Sensoriale

Ambient Intelligence (AmI) envisions a world where smart, electronic environments are aware and responsive to their context. People moving into these settings engage many computational devices and systems simultaneously even if they are not aware of their presence. AmI stems from the convergence of three key technologies: ubiquitous computing, ubiquitous communication and natural interfaces. The dependence on a large amount of fixed and mobile sensors embedded into the environment makes of Wireless Sensor Networks one of the most relevant enabling technologies for AmI. WSN are complex systems made up of a number of sensor nodes, simple devices that 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). 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. In order to handle the large amount of data generated by a WSN several multi sensor data fusion techniques have been developed. The aim of multisensor data fusion is to combine data to achieve better accuracy and inferences than could be achieved by the use of a single sensor alone. 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: Multimodal Surveillance and Activity Recognition. Novel techniques to handle data from a network of low-cost, low-power Pyroelectric InfraRed (PIR) sensors are presented. Such techniques allow the detection of the number of people moving in the environment, their direction of movement and their position. We discuss how a mesh of PIR sensors can be integrated with a video surveillance system to increase its performance in people tracking. Furthermore we embed a PIR sensor within the design of a Wireless Video Sensor Node (WVSN) to extend its lifetime. Activity recognition is a fundamental block in natural interfaces. A challenging objective is to design an activity recognition system that is able to exploit a redundant but unreliable WSN. We present our activity in building a novel activity recognition architecture for such a dynamic system. The architecture has a hierarchical structure where simple nodes performs gesture classification and a high level meta classifiers fuses a changing number of classifier outputs. We demonstrate the benefit of such architecture in terms of increased recognition performance, and fault and noise robustness. Furthermore we show how we can extend network lifetime by performing a performance-power trade-off. Smart objects can enhance user experience within smart environments. We present our work in extending the capabilities of the Smart Micrel Cube (SMCube), a smart object used as tangible interface within a tangible computing framework, through the development of a gesture recognition algorithm suitable for this limited computational power device. Finally the development of activity recognition techniques can greatly benefit from the availability of shared dataset. We report our experience in building a dataset for activity recognition. Such dataset is freely available to the scientific community for research purposes and can be used as a testbench for developing, testing and comparing different activity recognition techniques.

Multilevel Analysis in Household Survey: An Application to Health Condition Data

The aim of this thesis is to apply multilevel regression model in context of household surveys. Hierarchical structure in this type of data is characterized by many small groups. In last years comparative and multilevel analysis in the field of perceived health have grown in size. The purpose of this thesis is to develop a multilevel analysis with three level of hierarchy for Physical Component Summary outcome to: evaluate magnitude of within and between variance at each level (individual, household and municipality); explore which covariates affect on perceived physical health at each level; compare model-based and design-based approach in order to establish informativeness of sampling design; estimate a quantile regression for hierarchical data. The target population are the Italian residents aged 18 years and older. Our study shows a high degree of homogeneity within level 1 units belonging from the same group, with an intraclass correlation of 27% in a level-2 null model. Almost all variance is explained by level 1 covariates. In fact, in our model the explanatory variables having more impact on the outcome are disability, unable to work, age and chronic diseases (18 pathologies). An additional analysis are performed by using novel procedure of analysis :"Linear Quantile Mixed Model", named "Multilevel Linear Quantile Regression", estimate. This give us the possibility to describe more generally the conditional distribution of the response through the estimation of its quantiles, while accounting for the dependence among the observations. This has represented a great advantage of our models with respect to classic multilevel regression. The median regression with random effects reveals to be more efficient than the mean regression in representation of the outcome central tendency. A more detailed analysis of the conditional distribution of the response on other quantiles highlighted a differential effect of some covariate along the distribution.

Conductive Polymer Composites

In recent years, nanotechnologies have led to the production of materials with new and sometimes unexpected qualities through the manipulation of nanoscale components. This research aimed primarily to the study of the correlation between hierarchical structures of hybrid organic-inorganic materials such as conductive polymer composites (CPCs). Using a bottom-up methodology, we could synthesize a wide range of inorganic nanometric materials with a high degree of homogeneity and purity, such as thiol capped metal nanoparticles, stoichiometric geomimetic chrysotile nanotubes and metal dioxide nanoparticles. It was also possible to produce inorganic systems formed from the interaction between the synthesized materials. These synthesized materials and others like multiwalled carbon nanotubes and grapheme oxide were used to produce conductive polymer composites. Electrospinning causes polymer fibers to become elongated using an electric field. This technique was used to produce fibers with a nanometric diameter of a polymer blend based on two different intrinsically conducting polymers polymers (ICPs): polyaniline (PANI) and poly(3-hexylthiophene) (P3HT). Using different materials as second phase in the initial electrospun polymer fibers caused significant changes to the material hierarchical structure, leading to the creation of CPCs with modified electrical properties. Further study of the properties of these new materials resulted in a better understanding of the electrical conductivity mechanisms in these electrospun materials.

Analyzing the dependence structure of microarray data: a copula–based approach

The main aim of this Ph.D. dissertation is the study of clustering dependent data by means of copula functions with particular emphasis on microarray data. Copula functions are a popular multivariate modeling tool in each field where the multivariate dependence is of great interest and their use in clustering has not been still investigated. The first part of this work contains the review of the literature of clustering methods, copula functions and microarray experiments. The attention focuses on the K–means (Hartigan, 1975; Hartigan and Wong, 1979), the hierarchical (Everitt, 1974) and the model–based (Fraley and Raftery, 1998, 1999, 2000, 2007) clustering techniques because their performance is compared. Then, the probabilistic interpretation of the Sklar’s theorem (Sklar’s, 1959), the estimation methods for copulas like the Inference for Margins (Joe and Xu, 1996) and the Archimedean and Elliptical copula families are presented. In the end, applications of clustering methods and copulas to the genetic and microarray experiments are highlighted. The second part contains the original contribution proposed. A simulation study is performed in order to evaluate the performance of the K–means and the hierarchical bottom–up clustering methods in identifying clusters according to the dependence structure of the data generating process. Different simulations are performed by varying different conditions (e.g., the kind of margins (distinct, overlapping and nested) and the value of the dependence parameter ) and the results are evaluated by means of different measures of performance. In light of the simulation results and of the limits of the two investigated clustering methods, a new clustering algorithm based on copula functions (‘CoClust’ in brief) is proposed. The basic idea, the iterative procedure of the CoClust and the description of the written R functions with their output are given. The CoClust algorithm is tested on simulated data (by varying the number of clusters, the copula models, the dependence parameter value and the degree of overlap of margins) and is compared with the performance of model–based clustering by using different measures of performance, like the percentage of well–identified number of clusters and the not rejection percentage of H0 on . It is shown that the CoClust algorithm allows to overcome all observed limits of the other investigated clustering techniques and is able to identify clusters according to the dependence structure of the data independently of the degree of overlap of margins and the strength of the dependence. The CoClust uses a criterion based on the maximized log–likelihood function of the copula and can virtually account for any possible dependence relationship between observations. Many peculiar characteristics are shown for the CoClust, e.g. its capability of identifying the true number of clusters and the fact that it does not require a starting classification. Finally, the CoClust algorithm is applied to the real microarray data of Hedenfalk et al. (2001) both to the gene expressions observed in three different cancer samples and to the columns (tumor samples) of the whole data matrix.

Stellar Relics of the hierarchical assembly of the Galaxy

This PhD Thesis is part of a long-term wide research project, carried out by the "Osservatorio Astronomico di Bologna (INAF-OABO)", that has as primary goal the comprehension and reconstruction of formation mechanism of galaxies and their evolution history. There is now substantial evidence, both from theoretical and observational point of view, in favor of the hypothesis that the halo of our Galaxy has been at least partially, built up by the progressive accretion of small fragments, similar in nature to the present day dwarf galaxies of the Local Group. In this context, the photometric and spectroscopic study of systems which populate the halo of our Galaxy (i.e. dwarf spheroidal galaxy, tidal streams, massive globular cluster, etc) permits to discover, not only the origin and behaviour of these systems, but also the structure of our Galactic halo, combined with its formation history. In fact, the study of the population of these objects and also of their chemical compositions, age, metallicities and velocity dispersion, permit us not only an improvement in the understanding of the mechanisms that govern the Galactic formation, but also a valid indirect test for cosmological model itself. Specifically, in this Thesis we provided a complete characterization of the tidal Stream of the Sagittarius dwarf spheroidal galaxy, that is the most striking example of the process of tidal disruption and accretion of a dwarf satellite in to our Galaxy. Using Red Clump stars, extracted from the catalogue of the Sloan Digital Sky Survey (SDSS) we obtained an estimate of the distance, the depth along the line of sight and of the number density for each detected portion of the Stream (and more in general for each detected structure along our line of sight). Moreover comparing the relative number (i.e. the ratio) of Blue Horizontal Branch stars and Red Clump stars (the two features are tracers of different age/different metallicity populations) in the main body of the galaxy and in the Stream, in order to verify the presence of an age-metallicity gradient along the Stream. We also report the detection of a population of Red Clump stars probably associated with the recently discovered Bootes III stellar system. Finally, we also present the results of a survey of radial velocities over a wide region, extending from r ~ 10' out to r ~ 80' within the massive star cluster Omega Centauri. The survey was performed with FLAMES@VLT, to study the velocity dispersion profile in the outer regions of this stellar system. All the results presented in this Thesis, have already been published in refeered journals.

The physical city and the urban structure: detecting amenity zones and applying urban morphology to New York

The city is a collection of built structures and infrastructure embedded in socio-cultural processes: any investigation into a city’s transformations involves considerations on the degree to which its composite elements respond to socio-economical changes. The main purpose of this research is to investigate how transformations in the functional requirements of New York’s society have spurred, since the 1970s, changes in both the city’s urban structure and physical form. The present work examines the rise of Amenity Zones in New York, and investigates the transformations that have occurred in New York’s built environment since the 1970s. By applying qualitative measures and analyzing the relationship between urban amenities and the creative class, the present work has investigated changes in the urban structure and detected a hierarchical series of amenity zones classes, namely, Super Amenity Zones (SAZs), Nodal Amenity Zones (NAZs) and Peripheral Amenity Zones (PAZs). This series allows for a more comprehensive reading of the urban structure in a complex city like New York, bringing advancements to the amenity zone’s methodology. In order to examine the manner in which the other component of the city, the physical form, has changed or adapted to the new socio-economic condition, the present research has applied Conzenian analysis to a select study area, Atlantic Avenue. The results of this analysis reveal that, contrary to the urban structure, which changes rapidly, the physical form of New York is hard to modify completely, due to the resilience of the town plan and its elements, and to preservation laws; the city rather adapts to socio-economical changes through process of adaptive reuses or conversion. Concluding, this research has examined the dialectic between the ever-changing needs of society and the complexity of the built environment and urban structure, showing the different degrees to which the urban landscape modifies, reacts and sometimes adapts to the population’s functional requirements.