This thesis presents the outcomes of my Ph.D. course in telecommunications engineering. The focus of my research has been on Global Navigation Satellite Systems (GNSS) and in particular on the design of aiding schemes operating both at position and physical level and the evaluation of their feasibility and advantages. Assistance techniques at the position level are considered to enhance receiver availability in challenging scenarios where satellite visibility is limited. Novel positioning techniques relying on peer-to-peer interaction and exchange of information are thus introduced. More specifically two different techniques are proposed: the Pseudorange Sharing Algorithm (PSA), based on the exchange of GNSS data, that allows to obtain coarse positioning where the user has scarce satellite visibility, and the Hybrid approach, which also permits to improve the accuracy of the positioning solution. At the physical level, aiding schemes are investigated to improve the receiver’s ability to synchronize with satellite signals. An innovative code acquisition strategy for dual-band receivers, the Cross-Band Aiding (CBA) technique, is introduced to speed-up initial synchronization by exploiting the exchange of time references between the two bands. In addition vector configurations for code tracking are analyzed and their feedback generation process thoroughly investigated.

The Internet of Things (IoT) is the next industrial revolution: we will interact naturally with real and virtual devices as a key part of our daily life. This technology shift is expected to be greater than the Web and Mobile combined. As extremely different technologies are needed to build connected devices, the Internet of Things field is a junction between electronics, telecommunications and software engineering. Internet of Things application development happens in silos, often using proprietary and closed communication protocols. There is the common belief that only if we can solve the interoperability problem we can have a real Internet of Things. After a deep analysis of the IoT protocols, we identified a set of primitives for IoT applications. We argue that each IoT protocol can be expressed in term of those primitives, thus solving the interoperability problem at the application protocol level. Moreover, the primitives are network and transport independent and make no assumption in that regard. This dissertation presents our implementation of an IoT platform: the Ponte project. Privacy issues follows the rise of the Internet of Things: it is clear that the IoT must ensure resilience to attacks, data authentication, access control and client privacy. We argue that it is not possible to solve the privacy issue without solving the interoperability problem: enforcing privacy rules implies the need to limit and filter the data delivery process. However, filtering data require knowledge of how the format and the semantics of the data: after an analysis of the possible data formats and representations for the IoT, we identify JSON-LD and the Semantic Web as the best solution for IoT applications. Then, this dissertation present our approach to increase the throughput of filtering semantic data by a factor of ten.

Body-centric communications are emerging as a new paradigm in the panorama of personal communications. Being concerned with human behaviour, they are suitable for a wide variety of applications. The advances in the miniaturization of portable devices to be placed on or around the body, foster the diffusion of these systems, where the human body is the key element defining communication characteristics. This thesis investigates the human impact on body-centric communications under its distinctive aspects. First of all, the unique propagation environment defined by the body is described through a scenario-based channel modeling approach, according to the communication scenario considered, i.e., on- or on- to off-body. The novelty introduced pertains to the description of radio channel features accounting for multiple sources of variability at the same time. Secondly, the importance of a proper channel characterisation is shown integrating the on-body channel model in a system level simulator, allowing a more realistic comparison of different Physical and Medium Access Control layer solutions. Finally, the structure of a comprehensive simulation framework for system performance evaluation is proposed. It aims at merging in one tool, mobility and social features typical of the human being, together with the propagation aspects, in a scenario where multiple users interact sharing space and resources.

This thesis collects the outcomes of a Ph.D. course in Telecommunications engineering and it is focused on enabling techniques for Spread Spectrum (SS) navigation and communication satellite systems. It provides innovations for both interference management and code synchronization techniques. These two aspects are critical for modern navigation and communication systems and constitute the common denominator of the work. The thesis is organized in two parts: the former deals with interference management. We have proposed a novel technique for the enhancement of the sensitivity level of an advanced interference detection and localization system operating in the Global Navigation Satellite System (GNSS) bands, which allows the identification of interfering signals received with power even lower than the GNSS signals. Moreover, we have introduced an effective cancellation technique for signals transmitted by jammers, exploiting their repetitive characteristics, which strongly reduces the interference level at the receiver. The second part, deals with code synchronization. More in detail, we have designed the code synchronization circuit for a Telemetry, Tracking and Control system operating during the Launch and Early Orbit Phase; the proposed solution allows to cope with the very large frequency uncertainty and dynamics characterizing this scenario, and performs the estimation of the code epoch, of the carrier frequency and of the carrier frequency variation rate. Furthermore, considering a generic pair of circuits performing code acquisition, we have proposed a comprehensive framework for the design and the analysis of the optimal cooperation procedure, which minimizes the time required to accomplish synchronization. The study results particularly interesting since it enables the reduction of the code acquisition time without increasing the computational complexity. Finally, considering a network of collaborating navigation receivers, we have proposed an innovative cooperative code acquisition scheme, which allows exploit the shared code epoch information between neighbor nodes, according to the Peer-to-Peer paradigm.

Forest management is an important and complex process, which has significant implications on the envi-ronment (e.g. protection of biological diversity, climate mitigation) and the economy (e.g. estimation of timber volume for commercial usage). An efficient management requires a very detailed knowledge of forest attributes such as species composition, trees stem volume, height, etc. Hyperspectral and LIDAR remote sensing data can provide useful information to the identification of these attributes: hyperspectral data with their dense sampling of the spectral signatures are important for the classification of tree spe-cies, while LIDAR data are important for the study and estimation of quantitative parameters of forests (e.g. stem height, volume). This thesis presents novel systems for the exploitation of hyperspectral and LIDAR data in forest applica-tion domain. In particular, the novel contributions to the existing literature are on both the development of new systems for data processing and the analysis of the potentialities of these data in forestry. In greater detail the main contribution of this thesis are: i) an empirical analysis on the relationship be-tween spectral resolution, classifier complexity and classification accuracy in the study of complex forest areas. This analysis is very important for the design of future sensors and the better exploitation of the existing ones; ii) a novel system for the fusion of hyperspectral and LIDAR remote sensing data in the classification of forest areas. The system proposed exploits the complementary information of these data in order to obtain accurate and precise classification maps; iii) an analysis on the usefulness of different LIDAR returns and channels (elevantion and intensity) in the classification of forest areas; iv) an empiri-cal analysis on the use of multireturn LIDAR data for the estimation of tree stem volume. This study in-vestigates in detail the potentialities of variables extracted from LIDAR returns (up to four) for the esti-mation of tree stem volume; v) a novel system for the estimation of single tree stem diameter and volume with multireturn LIDAR data. A comparative analysis on the use of three different variable selection me-thods and three different estimation algorithms is also presented; vi) a system for the fusion of hyperspec-tral and LIDAR remote sensing data in the estimation of tree stem diameters. This system is able to ex-ploit hyperspectral and LIDAR data combined and separated: this is very important as the experimental analysis carried out with this system shows that hyperspectral data can be used for rough estimations of stem diameters when LIDAR data are not available. The effectiveness of all the proposed systems is confirmed by quantitative and qualitative experimental results.

The automatic understanding of human activity is probably one of the most challenging problems for the scientific community. Several application domains would benefit of such an analysis, from context-aware computing, to area monitoring and surveillance, to assistive technologies for elderly or disabled, and more. In a broad sense, we can define the activity analysis as the problem of finding an explanation coherent with a set of observations. These observations are typically influenced by several factors from different disciplines, such as sociology or psychology, but also mathematics and physics, making the problem particularly hard. In the last years, also the computer vision community focused its attention on this area, producing the latest advances in the acquisition and understanding of human motion data from image sequences. Despite the increasing effort spent in this field, there still exists a consistent gap between the numerical low-level pixel information that can be observed and measured, and the high abstraction level of the semantic that describes a given activity. In other words, there exist a conceptual ambiguity between the image sequence observations and their possible interpretations. Although several factors are involved, the activity modeling and the comparison strategy play crucial roles. In this proposal, a correlation between activity and corresponding path has been assumed. In light of this, the work carried out tackles two strictly related issues: (i) obtaining a proper representation of human activity; (ii) define an effective tool for reliably measuring the similarity between activity instances. In particular, the object activity is modeled with a signature obtained through a symbolic abstraction of its spatio-temporal trace, allowing the application of particular high-level reasoning for computing the activity similarity. This representation is particularly effective since it provides a smart way to compensate the noise artifacts coming from low-level modules (i.e., tracking algorithms), allowing also the possibility of considering interesting properties, such as the invariance to shift, rotation, and scale factors. Since any complex task may be decomposed in a limited set of atomic units corresponding to elementary motion patterns, the key idea of this representation is to catch the object activities by suitably representing their trajectories through symbols. This syntactic activity description relies on the extraction and on the symbolic coding of meaningful samples of the path, while the similarity between trajectories is computed using the so-called approximate-matching, thus casting the trajectory comparison problem to a string matching one. Also another representation scheme has been adopted, coding the signature according some relevant spots in the environment: in this case, the structural pattern information is coded in ad-hoc Context-Free Grammars, and the matching problem is solved through the parsing of the incoming string according the defined rules.

As the traffic volume on the Internet increases exponentially, so does the demand for fast switching of packets between asynchronous high-speed routers. Although the optical fiber can provide an extremely high capacity, the Internet switches still remain the main point of traffic bottleneck. The packet switching time may run up to nanoseconds in such routers with more than thousands ports, each processing at 10 GB/s. Even modern extremely fast processing units are not capable to satisfy these needs. It is well known that switching of such a high volume of traffic from input to output requires large buffers and fast processors to perform the header processing, complex scheduling and forwarding functions. Although a large number of switching architectures is presented on the market, the considerable part of them is either not scalable or reach their limits in power consumption and complexity. Therefore, novel and extremely scalable switching systems are essential to be investigated. The load-balancing switching approach is simple, and therefore, may be capable of performing the switching and forwarding from all inputs to all outputs simultaneously with low complexity and high scalability. Since this simple approach has distributed topology (each component of the switch is controlled by an individual chip) and do not require fast switch control units, primarily because each stage is independent and it makes its own distributed calculations, it becomes a perfect candidate for the future practical deployment. The load-balancing switching architecture, considered in this thesis, is proved to have high potential to scale up while maintaining good throughput and other performance characteristics. Additionally, the load-balancing switching architecture can effectively resolve the important problem of packets mis-ordering which can appear due to the distributed structure of the system. Unfortunately, in the research conducted previously, some of the mentioned characteristics were obtained under a set of strong assumptions. In particular, it was assumed that all the packets transmitted through the system have equal length, traffic is admissible and central stage buffers are infinite. On the other hand, due to the distributed control the switch is not able to control and maintain a necessary amount of traffic transmitted from stage to stage inside the switch. The following Ph.D. thesis analyzes behavior of the load-balancing switch equipped with finite central stage buffers. Due to this fact the LB switch will always have a possibility to drop a packet due to an overflow. In this work we first analyze the packet loss probability in the central stage buffers while considering packets of the same length (data cells). The analysis will be performed for both admissible and inadmissible traffic matrices. The obtained results show that the packet loss can have a significant influence on the overall LB switch performance if inputs of the switch are overloaded. In order to present more realistic scenario, the packet loss analysis was performed in the switch with variable size packets. It is considered that most of the internet switches are operating on the cell-based level (to increase buffer utilization), that means that arriving variable size packets are segmented at inputs and reassembled at outputs. The issue of possible cell and correspondingly a packet loss inside the switch can introduce some significant posterior problems to the load-balancing switch reassembly unit. In order to evaluate packet loss we assumed Markovian behavior to be able to use numerically efficient algorithms to solve the model. The mathematical model characterizing inhomogeneous input traffic presented inside the thesis gives the most precise way of packet loss probability evaluation. Unfortunately, the high complexity of this model results in irresolvable complex Markov chains even in case of very small switches. Consequently, as a next step, we performed the analysis with fast solution procedures using a restrictive assumption of identical stochastic processes at all inputs. The final results allowed us to conclude that a single cell drop at the central stage buffers cause the whole packet removal and, the packet loss probability inside the system can be extremely high in comparison with the corresponding cell loss. Another important issue observed from the analysis is the difference in packet loss probabilities depending on the traffic traversing path, e.g. sequential number of input, central stage buffer and output of the switch. This property makes more complex the evaluation of the loss probabilities for large switch sizes. The last but not the least issue observed by our analysis was the instability, congestion and large delays appearing at output re-sequencing and reassembly unit due to the the central stage packet loss. In order to cope with such a behavior, we proposed the novel algorithms which are able to efficiently minimize/avoid packet loss at the central stage buffers of the switch. For instance, the novel minimization protocol is introducing an artificial buffering threshold at the central stage buffers in such a way that packets at the input stage are are dropped in case the actual central stage buffers occupancy is above the threshold. The results show that due to possible packet removal at the input stage of the switch, the overall packet loss probability is significantly reduced. Similarly to the loss minimization service protocol, the novel NoLoss load-balancing switch operates while using information from both inputs and central stage buffers, and allows a packet transmission through the switch only if the central stage buffers have enough space to accept it during the current and the following time slots. In order to minimize communication overheads, the algorithm was implemented by means of centralize controller. Finally, such kind of management helped us to reach the lower boundary in the overall packet loss probability and resolve some other important issues of the switch, like, for instance, the congestion problem of the output reassembly unit.

Internet was originally designed to offer best-effort data transport over a wired network with end machines using a layered network protocol stack to provide mainly reliability and quality of service for end user applications. However, the excess of wireless end devices and the demand for sophisticated mobile multimedia applications forces the networking research community to think about new design methodologies. In fact, this kind of applications is characterized not only by a large amount of required data-rate, but also by a significant variability of the data-rate over time due to the dynamics of scenes, when state of the art of video encoding techniques are considered and are especially challenging due to the time varying transmission characteristics of the wireless channel and the dynamic quality of service (QoS) requirements of the application (e.g., prioritized delivery of important units, variable bit rate and variable tolerance vs. bit or packet errors). One of the focused issues in the improvement of multimedia transmission quality is to combine the characteristics of the video applications and the wireless networks. Traditional approaches, in which the characteristics of the video application and wireless networks are isolated, would induce the resources not being optimized. Cross-layer design also known as Cross-layering is a new paradigm in network design that not only takes the dependencies and interactions among the layers of the Open System Interface (OSI) structure into account, but also attains a global optimization of the layer-specific parameters. However, most existing cross-layer designs for Quality of Service (QoS) provisioning in multimedia communications are mainly either aiming at improving throughput of the network or reducing power consumption, yet regardless of the end-toend qualities of multimedia transmission. Therefore, the application-driven cross-layer design over various multimedia communication systems is needed to be extensively investigated. Following the extensive study of performance bounds and limitations of the sate of the art in this research area, we argue that performance improvement of multimedia applications over wireless access networks can be achieved through considering the application-specific requirements also called service- or context-awareness. Indeed, we designed two cross-layer design schemes called CORREC and SARC for Wi-Fi and 3G networks respectively. We show that further performance improvement can be achieved by tuning ARQ and HARQ strength respectively based on the application requirements and protocol stack operation on the mobile terminal. In the other hand, the Transmission Control Protocol (TCP) which accounts for over 95% of Internet traffic shows poor performance in wireless domain. We propose a novel approach aiming at TCP performance improvement in WLAN networks. It consists in proposing a joint optimization of ARQ schemes operating at the transport and link layers using a cross-layer approach called ARQ proxy for Wi-Fi networks.

The increasing availability of new generation remote sensing satellite multispectral images provides an unprecedented source of information for Earth observation and monitoring. Multispectral images can be now collected at high resolution covering (almost) all land surfaces with extremely short revisit time (up to a few days), making it possible the mapping of global changes. Extracting useful information from such huge amount of data requires a systematic use of automatic techiques in almost all applicative contexts. In some cases, the strict application requirements force the pratictioner to develop strongly data-driven approaches in the development of the processing chain. As a consequence, the exact relationship between the theoretical models adopted and the physical meaning of the solutions is sometimes hidden in the data analysis techniques, or not clear at all. Altough this is not a limitation for the success of the application itself, it makes however dicult to transfer the knowledge learned from one specic problem to another. In this thesis we mainly focus on this aspect and we propose a general mathematical framework for the representation and analysis of multispectral images. The proposed models are then used in the applicative context of change detection. Here, the generality of the proposed models allows us to both: (1) provide a mathematical explanation of already existing methodologies for change detection, and (2) extend them to more general cases for addressing problems of increasing complexity. Typical spatial/spectral properties of last generation multispectral images emphasize the need of having more exible models to image representation. In fact, classical methods to change detection that have worked well on previous generations of multispectral images provide sub-optimal results due to their poor capability of modeling all the complex spectral/spatial detail available in last generation products. The theoretical models presented in this thesis are aimed at giving more degrees of freedom in the representation of the images. The eectiveness of the proposed novel approaches and related techniques is demonstrated on several experiments involving both synthetic datasets and real multispectral images. Here, the improved flexibility of the models adopted allows for a better representation of the data and is always followed by a substantial improvement of the change detection performance.

In the last decade the increasing availability of high resolution remote sensing data enabled precision forestry, which aims to obtain a precise reconstruction of the forest at stand, sub-stand or individual tree level. This calls for the need of developing techniques tailored on such new data that can achieve accurate forest parameters estimations. Moreover, in this context the integration of multiple remote sensing data brings to a more comprehensive representation of the forest structure. Accordingly, the goal of this thesis is the development of novel methods for the automatic estimation of forest parameters that can exploit the different properties of multiple remote sensing data sources. The thesis provides five main novel contributions to the state-of-the-art. The first contribution of the thesis addresses the problem of the single tree crowns segmentation in multilayered forest by using very high-density multireturn LiDAR data. The aim of the proposed method is to fully exploit the potential of these data to detect and delineate the single tree crowns of both dominant and sub-dominant trees by a hierarchical 3-D segmentation technique applied directly in the point cloud space. The second contribution of the thesis regards the estimation of the diameter at breast height (DBH) of each individual tree by using high-density LiDAR data. The proposed data-driven method extensively exploits the information provided by the high resolution data to model the main environmental variables that can affect the stems growth in terms of crown structure, topography and forest density. The third contribution of the thesis proposes a 3-D model based approach to the reconstruction of the tree top height by fusing low-density LiDAR data and high resolution optical images. The geometrical structure of the tree is reconstructed via a properly defined parametric model which drives the fusion of the data. Indeed, when high resolution LiDAR data is not available, the integration of different remote sensing data sources represents a valid solution to improve the parameter estimation. In this context, the fourth contribution of the thesis addresses the fusion of low-density airborne LiDAR data and terrestrial LiDAR data to perform localized forest analysis. The proposed technique automatically registers the two LiDAR point clouds by using the spatial pattern of the forest in order to integrate the data and to automatically estimate the crown parameters. The fusion of the LiDAR point clouds leads to a more comprehensive representation of the 3-D structure of the crowns. Finally, we introduce a sensor-driven domain adaptation method for the classification of forest areas sharing similar properties but located in different areas. The proposed method takes advantage from the availability of multiple remote sensing data to detect features subspaces where data manifolds are partially (or completely) aligned. Qualitative and quantitative experimental results obtained on large forest areas confirm the effectiveness of the methods developed in this thesis, which allow an improvement in terms of accuracy when compared to other state-of-the-art methods.