Time synchronization is of primary importance for the operation of wireless sensor networks (WSN): time measurements, coordinated actions and event ordering require common time on WSN nodes. Due to intrinsic energy limitations of wireless networks there is a need for new energy-efficient time synchronization solutions, different from the ones that have been developed for wired networks. In this work we investigated the trade-offs between time synchronization accuracy and energy saving in WSN. On the basis of that study we developed a power-efficient adaptive time synchronization strategy, that achieves a target synchronization accuracy at the expense of a negligible overhead. Also, we studied the energy benefits of periodic time synchronization in WSN employing synchronous wakeup schemes, and developed an algorithm that finds the optimal synchronization period to save energy. The proposed research improves state-of-the-art by exploring new ways to save energy while assuring high flexibility and reliable operation of WSN.

The widespread use of digital cameras, as well as the increasing popularity of online photo sharing has led to the proliferation of networked photo collections. Handling such a huge amount of media, without imposing complex and time consuming archiving procedures, is highly desirable and poses a number of interesting research challenges to the media community. In particular, the definition of suitable content based indexing and retrieval methodologies is attracting the effort of a large number of researchers worldwide, who proposed various tools for automatic content organization, retrieval, search, annotation and summarization. In this thesis, we will present and discuss three different approaches for content-and-context based retrieval. The main focus will be put on personal photo albums, which can be considered one of the most challenging application domains in this field, due to the largely unstructured and variable nature of the datasets. The methodologies that we will describe can be summarized into the following three points: i. Stochastic approaches to exploit the user interaction in query-by-example photos retrieval. Understanding the subjective meaning of a visual query, by converting it into numerical parameters that can be extracted and compared by a computer, is the paramount challenge in the field of intelligent image retrieval, also referred to as the “semantic gap†problem. An innovative approach is proposed that combines a relevance feedback process with a stochastic optimization engine, as a way to grasp user's semantics through optimized iterative learning providing on one side a better exploration of the search space, and on the other side avoiding stagnation in local minima during the retrieval. ii. Unsupervised event collection, segmentation and summarization. The need for automatic tools able to extract salient moments and provide automatic summary of large photo galleries is becoming more and more important due to the exponential growth in the use of digital media for recording personal, familiar or social life events. The multi-modal event segmentation algorithm faces the summarization problem in an holistic way, making it possible to exploit the whole available information in a fully unsupervised way. The proposed technique aims at providing such a tool, with the specific goal of reducing the need of complex parameter settings and letting the system be widely useful for as many situations as possible. iii. Content-based synchronization of multiple galleries related to the same event. The large spread of photo cameras makes it quite common that an event is acquired through different devices, conveying different subjects and perspectives of the same happening. Automatic tools are more and more used to support the users in organizing such archives, and it is largely accepted that time information is crucial to this purpose. Unfortunately time-stamps may be affected by erroneous or imprecise setting of the camera clock. The synchronization algorithm presented is the first that uses the content of pictures to estimate the mutual delays among different cameras, thus achieving an a-posteriori synchronization of various photo collections referring to the same event.

The study, development, and analysis of innovative inversion techniques for the detection and imaging of buried objects is addressed in this thesis. The proposed methodologies are based on the use of microwave radiations and radar techniques for subsurface prospecting, such as, for example, the Ground Penetrating Radar (GPR). More precisely, the reconstruction of shallow buried objects is firstly addressed by an electromagnetic inverse scattering method based on the integration of the inexact Newton (IN) method with an iterative multiscaling approach. The performances of such an inversion approach are analyzed both when considering the use of a second-order Born approximation (SOBA) and when exploiting the full set of non-linear equations governing the scattering phenomena for the buried scenario. The presented methodologies are particularly suitable for applications such as demining (e.g., for the detection of unexploded ordnances, UXOs, and improvised explosive devices, IEDs), for civil engineering applications (e.g., for the investigation of possible structural damages, voids, cracks or water infiltrations in walls, pillars, bridges) as well as for biomedical imaging (e.g., for early cancer detection).

In the last decade the interest in radar systems for the exploration of planetary bodies and for Earth Observation (EO) from orbit increased considerably. In this context, the main goal of this thesis is to present novel methods for the automatic analysis of planetary radar sounder (RS) signals and very high resolution (VHR) synthetic aperture radar (SAR) images acquired on the Earth. Both planetary RSs and VHR SAR systems are instruments based on relatively recent technology which make it possible to acquire from orbit new types of data that before were available only in limited areas from airborne acquisitions. The use of orbiting platforms allows the acquisition of a huge amount of data on large areas. This calls for the development of effective and automatic methods for the extraction of information tuned on the characteristics of these new systems. The work has been organized in two parts. The first part is focused on the automatic analysis of data acquired by planetary RSs. RS signals are currently mostly analyzed by means of manual investigations and the topic of automatic analysis of such data has been only marginally addressed in the literature. In this thesis we provide three main novel contributions to the state of the art on this topic. First, we present a theoretical and empirical statistical study of the properties of RS signals. Such a study drives the development of two novel automatic methods for the generation of subsurface feature maps and for the detection of basal returns. The second contribution is a method for the extraction of subsurface layering in icy environments, which is capable to detect linear features with sub-pixel accuracy. Moreover, measures for the analysis of the properties of the detected layers are proposed. Finally, the third contribution is a technique for the detection of surface clutter returns in radargrams. The proposed method is based on the automatic matching between real and clutter data generated according to a simulator developed in this thesis. The second part of this dissertation is devoted to the analysis of VHR SAR images, with special focus on urban areas. New VHR SAR sensors allow the analysis of such areas at building level from space. This is a relatively recent topic, which is especially relevant for crisis management and damage assessment. In this context, we describe in detail an empirical and theoretical study carried out on the relation between the double-bounce effect of buildings and their orientation angle. Then, a novel approach to the automatic detection and reconstruction of building radar footprints from VHR SAR images is pre-sented. Unlike most of the methods presented in the literature, the developed method can extract and reconstruct building radar footprints from single VHR SAR images. The technique is based on the detection and combination of primitive features in the image, and introduces the concept of semantic meaning of the primitives. Qualitative and quantitative experimental results obtained on real planetary RS and spaceborne VHR SAR data confirm the effectiveness of the proposed methods.

Future mobile broadband communications working over wireless channels are required to provide high performance services in terms of speed, capacity, and quality. A key issue to be considered is the design of multi-standard and multi-modal ad-hoc network architectures, capable of self-configuring in an adaptive and optimal way with respect to channel conditions and traffic load. In the context of 4G-wireless communications, the implementation of efficient baseband receivers characterized by affordable computational load is a crucial point in the development of transmission systems exploiting diversity in different domains. This thesis proposes some novel multi-user detection techniques based on different criterions (i.e., MMSE, ML, and MBER) particularly suited for multi-carrier CDMA systems, both in the single- and multi-antenna cases. Moreover, it considers the use of evolutionary strategies (such as GA and PSO) to channel estimation purposes in MIMO multicarrier scenarios. Simulation results evidenced that the proposed PHY-layer optimization techniques always outperform state of the art schemes by spending an affordable computational burden. Particular attention has been used on the software implementation of the formulated algorithms, in order to obtain a modular software architecture that can be used in an adaptive and optimized reconfigurable scenario.

The study, design and development of a monitoring system for wildlife road crossing problem is addressed in this thesis. Collisions between fauna and vehicles is a relevant issue in several mountain and rural regions and a valuable low-cost solution has not yet been identified. In particular, the proposed system is composed by a network of sensors installed along road margins, in order to detect wildlife events, (e.g., approaching, leaving or crossing the road), thus to promptly warn the incoming drivers. The sensor nodes communicate wirelessly among the network thus collecting the sensed information in a control unit for data storage, processing and statistics. The detection process is performed by the wireless nodes, which are equipped with low-cost Doppler radars for real-time identification of wildlife movements. In detail, different technologies valuable for solving the problem and related off-the-shelf solutions have been investigated and properly tested in order to validate their actual performance considering the specific problem scenario. A final classification based on specific parameters has allowed identifying the Doppler radar system as the better low-cost technology for contributing to the problem objective. The performance of the proposed system has also been investigated in a real scenario, which has been identified to be the actual pilot site for the monitoring system. This confirms the system capability of movements detection in the road proximity, thus defining a security area along it, where all occurring events may be identified.

The deployment of low power wireless networks is notoriously effort-demanding, as costly in-field campaigns are required to assess the connectivity properties of the target location and understand where to place the wireless nodes. The characteristics of the environment, both static (e.g., obstacles obstructing the link line of sight) and dynamic (e.g., changes in weather conditions) cause variability in the communication performance, thus affecting the network operation quality and reliability. This translates into difficulties in effectively deploy, plan and manage these networks in real-world scenarios, especially outdoor. Despite the large literature on node placement, existing approaches make over-simplifying assumptions neglecting the complexity of the radio environment. Airborne and satellite Remote Sensing (RS) systems acquire data and images over wide areas, thus enabling one to derive information about these areas at large scale. In this dissertation, we propose to leverage RS systems and related data processing techniques to i) automatically derive the static characteristics of the deployment environment that affect low power wireless communication; ii) model the relation between such characteristics and the communication quality; and iii) exploit this knowledge to support the deployment planning. We focus on two main scenarios: a) the deployment of Wireless Sensor Networks (WSNs) in forests; and b) the communication performance of Internet of Things (IoT) networks based on Long Range (LoRa) wireless technology in the presence of mixed environments. As a first major contribution, we propose a novel WSN node placement approach (LaPS) that integrates remote sensing data acquired by airborne Light Detection and Ranging (LiDAR) instruments, a specialized path loss model and evolutionary computation to identify (near-)optimal node position in forests, automatically and prior to the actual deployment. When low-power WSNs operating at 2.4 GHz are deployed in forests, the presence of trees greatly affects communication. We define a processing architecture that automatically derives local forest attributes (e.g., tree density) from LiDAR data acquired over the target forest. This information is incorporated into a specialized path loss model, which is validated in deployments in a real forest, enabling fine-grained, per-link estimates of the radio signal attenuation induced by trees. Combining the forest attributes derived from LiDAR data with the specialized path loss model and a genetic algorithm, LaPS provides node placement solutions with higher quality than approaches based on a regular placement or on a standard path loss model, while satisfying the spatial and network requirements provided by the user. In addition, LaPS enables the exploration of the impact of changes in the user requirements on the resulting topologies in advance, thus reducing the in-field deployment effort. Moreover, to explore a different low-power wireless technology with starkly different trade-offs, we consider a LoRa-based IoT network operating in i) a free space like communication environment, i.e., the LoRa signal is transmitted from an high altitude weather balloon, traverses a free-of-obstacles space and is received by gateways on the ground; and ii) a mixed environment that contains built-up areas, farming fields and groups of trees, with both LoRa transmitters and receiving gateways close to the ground. These scenarios show a huge gap in terms of communication range, thus revealing to which extent the presence of objects affects the coverage that LoRa gateways can provide. To characterize the mixed environment we exploit detailed land cover maps (i.e., with spatial grain 10x10m2) derived by automatically classifying multispectral remote sensing satellite images. The land cover information is jointly analyzed with LoRa connectivity traces, enabling us to observe a correlation between the land cover types involved in LoRa links and the trend of the signal attenuation with the distance. This analysis opens interesting research venues aimed at defining LoRa connectivity models that quantitatively account for the type of environment involved in the communication by leveraging RS data.

Reflectarray antennas are reflector structures which combine characteristics of both reflector and array antennas. They exhibit electrically large apertures in order to generate significant gain as conventional metallic reflector antennas. At the same time they are populated by several radiating elements which can be controlled individually like conventional phased array antennas. They are usually flat and can be folded and deployed permitting important saving in terms of volume. For these reasons they have been considered since several years for satellite applications. Initially constituted by truncated metallic waveguides and mainly considered for radar applications, they are now mainly constituted by a dielectric substrate, backed by a metallic plane (groundplane) on which microstrip elements with variable shape/size/orientation are printed. These elements are illuminated by the primary feed. The reflected wave from each element has a phase that can be controlled by the geometry of the element itself. By a suitable design of the elements that make up the reflectarray, it is therefore possible to compose the phase front of the reflected waves in the desired direction (steering direction), and to ensure that the obtained overall radiation pattern exhibits a secondary lobe profile which meets the design specifications. Reflectarrays may be used to synthesize pencil or shaped beams. The synthesis methods commonly used to achieve this goal are based on three different steps: (a) calculation of the nearfield “phase distribution” that the wave reflected by the reflectarray must exhibit to get the desired far-field behaviour; (b) discretization of such distribution into cells of size comparable to that of the elements of interest (i.e., the patches); (c) calculation of the geometry of each elementary cell that will provide the desired reflection coefficient. The first step (a) is a Phase Only approach and permits already to achieve fast preliminary indications on the performance achievable. Accurate results require the implementation of the steps (b) and (c) as well and it is thus of fundamental importance to have techniques capable of efficiently and accurately calculating the reflection coefficient associated with a given geometry of the element [in order to efficiently solve the step (c)]. This coefficient is mathematically represented by a 2x2 complex matrix, which takes into account the relationships between co-polar and cross-polar components of the incident (due to the feed) and reflected field. This matrix naturally depends on the geometry of the element, the direction of incidence of the wave (azimuth and elevation) and the operating frequency of the system. The computation of the reflection coefficient is usually performed using electromagnetic full-wave (FW) simulators; the computation is however time consuming and the generation of the unit cells scattering response database becomes often unfeasible. In this work, an innovative strategy based on an advanced statistical learning method is introduced to efficiently and accurately predict the electromagnetic response of complex-shaped reflectarray elements. The computation of the scattering coefficients of periodic arrangements, characterized by an arbitrary number of degrees-of-freedom, is firstly recast as a vectorial regression problem, then solved with a learning-by-example strategy exploiting the Ordinary Kriging paradigm. A set of representative numerical experiments dealing with different element geometries is presented to assess the accuracy, the computational efficiency, and the flexibility of the proposed technique also in comparison with state-of-the-art machine learning methods.

This doctoral thesis consists of three essays within the field of economics of information privacy examined through the lens of behavioral and experimental economics. Rapid development and expansion of Internet, mobile and network technologies in the last decades has provided multitudinous opportunities and benefits to both business and society proposing the customized services and personalized offers at a relatively low price and high speed. However, such innovations and progress have also created complex and hazardous issues. One of the main problems is related to the management of extensive flows of information, containing terabytes of personal data. Collection, storage, analysis, and sharing of this information imply risks and trigger usersâ concerns that range from nearly harmless to significantly pernicious, including tracking of online behavior and location, intrusive or unsolicited marketing, price discrimination, surveillance, hacking attacks, fraud, and identity theft. Some users ignore these issues or at least do not take an action to protect their online privacy. Others try to limit their activity in Internet, which in turn may inhibit the online shopping acceptance. Yet another group of users gathers personal information protection, for example, by deploying the privacy-enhancing technologies, e.g., ad-blockers, e-mail encryption, etc. The ad-blockers sometimes reduce the revenue of online publishers, which provide the content to their users for free and do not receive the income from advertisers in case the user has blocked ads. The economics of privacy studies the trade-offs related to the positive and negative economic consequences of personal information use by data subjects and its protection by data holders and aims at balancing the interests of both parties optimising the expected utilities of various stakeholders. As technology is penetrating every aspect of human life raising numerous privacy issues and affecting a large number of interested parties, including business, policy-makers, and legislative regulators, the outcome of this research is expected to have a great impact on individual economic markets, consumers, and society as a whole. The first essay provides an extensive literature review and combines the theoretical and empirical evidence on the impact of advertising in both traditional and digital media in order to gain the insights about the effects of ad-blocking privacy-enhancing technologies on consumersâ welfare. It first studies the views of the main schools of advertising, informative and persuasive. The informative school of advertising emphasizes the positive effects of advertising on sales, competition, product quality, and consumersâ utility and satisfaction by matching buyers to sellers, informing the potential customers about available goods and enhancing their informed purchasing decisions. In contrast, the advocates of persuasive school view advertising as a generator of irrational brand loyalty that distorts consumersâ preferences, inflates product prices, and creates entry barriers. I pay special attention to the targeted advertising, which is typically assumed to have a positive impact on consumersâ welfare if it does not cause the decrease of product quality and does not involve the extraction of consumersâ surplus through the exploitation of reservation price for discriminating activities. Moreover, the utility of personalized advertising appears to be a function of its accuracy: the more relevant is a targeted offer, the more valuable it is for the customer. I then review the effects of online advertising on the main stakeholders and users and show that the low cost of online advertising leads to excessive advertising volumes causing information overload, psychological discomfort and reactance, privacy concerns, decreased exploration activities and opinion diversity, and market inefficiency. Finally, as ad-blocking technologies filter advertising content and limit advertising exposure, I analyze the consequences of ad-blocking deployment through the lens of the models on advertising restrictions. The control of advertising volume and its partial restriction would benefit both consumers and businesses more than a complete ban of advertising. For example, advertising exposure caps, which limit the number of times that the same ad is to be shown to a particular user, general reduction of the advertising slots, control of the advertising quality standards, and limitation of tracking would result in a better market equilibrium than can offer an arms race of ad-blockers and anti-ad-blockers. Finally, I review the solutions alternative to the blocking of advertising content, which include self regulation, non-intrusive ads programs, paywall, intention economy approach that promotes business models, in which user initiates the trade and not the marketer, and active social movements aimed at increasing social awareness and consumer education. The second essay describes a model of factors affecting Internet usersâ perceptions of websitesâ trustworthiness with respect to their privacy and the intentions to purchase from such websites. Using focus group method I calibrate a list of websitesâ attributes that represent those factors. Then I run an online survey with 117 adult participants to validate the research model. I find that privacy (including awareness, information collection and control practices), security, and reputation (including background and feedback) have strong effect on trust and willingness to buy, while website quality plays a marginal role. Although generally trustworthiness perceptions and purchase intentions are positively correlated, in some cases participants are likely to purchase from the websites that they have judged as untrustworthy. I discuss how behavioral biases and decision-making heuristics may explain this discrepancy between perceptions and behavioral intentions. Finally, I analyze and suggest what factors, particular websitesâ attributes, and individual characteristics have the strongest effect on hindering or advancing customersâ trust and willingness to buy. In the third essay I investigate the decision of experimental subjects to incur the risk of revealing personal information to other participants. I do so by using a novel method to generate personal information that reliably induces privacy concerns in the laboratory. I show that individual decisions to incur privacy risk are correlated with decisions to incur monetary risk. I find that partially depriving subjects of control over the revelation of their personal information does not lead them to lose interest in protecting it. I also find that making subjects think of privacy decisions after financial decisions reduces their aversion to privacy risk. Finally, surveyed attitude to privacy and explicit willingness to pay or to accept payments for personal information correlate with willingness to incur privacy risk. Having shown that privacy loss can be assimilated to a monetary loss, I compare decisions to incur risk in privacy lotteries with risk attitude in monetary lotteries to derive estimates of the implicit monetary value of privacy. The average implicit monetary value of privacy is about equal to the average willingness to pay to protect private information, but the two measures do not correlate at the individual level. I conclude by underlining the need to know individual attitudes to risk to properly evaluate individual attitudes to privacy as such.

Knowing how to exploit unlabeled data is a fundamental problem in machine learning. This dissertation provides contributions in different contexts, including semi-supervised learning, positive unlabeled learning and representation learning. In particular, we ask (i) whether is possible to learn a classifier in the context of limited data, (ii) whether is possible to scale existing models for positive unlabeled learning, and (iii) whether is possible to train a deep generative model with a single minimization problem.