Ultra-low power WSNs: distributed signal processing and dynamic resource management

This thesis presents several data processing and compression techniques capable of addressing the strict requirements of wireless sensor networks. After introducing a general overview of sensor networks, the energy problem is introduced, dividing the different energy reduction approaches according to the different subsystem they try to optimize. To manage the complexity brought by these techniques, a quick overview of the most common middlewares for WSNs is given, describing in detail SPINE2, a framework for data processing in the node environment. The focus is then shifted on the in-network aggregation techniques, used to reduce data sent by the network nodes trying to prolong the network lifetime as long as possible. Among the several techniques, the most promising approach is the Compressive Sensing (CS). To investigate this technique, a practical implementation of the algorithm is compared against a simpler aggregation scheme, deriving a mixed algorithm able to successfully reduce the power consumption. The analysis moves from compression implemented on single nodes to CS for signal ensembles, trying to exploit the correlations among sensors and nodes to improve compression and reconstruction quality. The two main techniques for signal ensembles, Distributed CS (DCS) and Kronecker CS (KCS), are introduced and compared against a common set of data gathered by real deployments. The best trade-off between reconstruction quality and power consumption is then investigated. The usage of CS is also addressed when the signal of interest is sampled at a Sub-Nyquist rate, evaluating the reconstruction performance. Finally the group sparsity CS (GS-CS) is compared to another well-known technique for reconstruction of signals from an highly sub-sampled version. These two frameworks are compared again against a real data-set and an insightful analysis of the trade-off between reconstruction quality and lifetime is given.

La permanence du picaresque dans la littérature contemporaine

La problématique critique autour de la permanence du picaresque à l’époque contemporaine révèle, à partir des années soixante, une controverse qui n’est pas parvenue à en donner une vision homogène. L’oscillation entre une conception historique close et une conception a-historique ouverte ne permet pas de saisir les données essentielles d’une présence très riche et qui n’a pas du tout disparu. Pour le démontrer, on a pris en considération deux ensembles d’œuvres des XXe et XXIe siècles qui présentent un caractère bien distinct : d’un côté, un corpus restreint, composé par des réécritures ou des adaptations des textes canoniques espagnols ; de l’autre, un deuxième corpus plus riche en termes quantitatifs, qui ne représente pas forcement une réélaboration du canon du genre picaresque. Pour aborder l’analyse du corpus, on a évidemment essayé d’identifier des caractéristiques spécifiques qui ont survécu, tout en subissant parfois des transformations, au cours des XXe et XXIe siècles : plus particulièrement, on a pris en considération le narrateur, le motif de la naissance ignoble, la marginalisation du héros et son statut dynamique, aussi bien que la conclusion du récit. D’une tel analyse, il émerge en définitive que la réactivation du genre picaresque ne se borne pas à une réécriture contemporaine, mais aussi qu’il constitue un genre dont la survivance ne peut être mise en question, et dont la diffusion est assez ample. L’analyse d’une telle permanence dans la littérature contemporaine permet de comprendre que ce genre n’est pas lié exclusivement à une société particulière et à un moment historique précis, mais qu’il relève d’une structure plus profonde qui réussit à s’incarner, au cours de l’histoire, dans l’écriture et dans la tradition littéraire.

Statistical mechanics formalism and methods for the analysis of real networks

This thesis provides a thoroughly theoretical background in network theory and shows novel applications to real problems and data. In the first chapter a general introduction to network ensembles is given, and the relations with “standard” equilibrium statistical mechanics are described. Moreover, an entropy measure is considered to analyze statistical properties of the integrated PPI-signalling-mRNA expression networks in different cases. In the second chapter multilayer networks are introduced to evaluate and quantify the correlations between real interdependent networks. Multiplex networks describing citation-collaboration interactions and patterns in colorectal cancer are presented. The last chapter is completely dedicated to control theory and its relation with network theory. We characterise how the structural controllability of a network is affected by the fraction of low in-degree and low out-degree nodes. Finally, we present a novel approach to the controllability of multiplex networks

Matrix Designs and Methods for Secure and Efficient Compressed Sensing

The idea of balancing the resources spent in the acquisition and encoding of natural signals strictly to their intrinsic information content has interested nearly a decade of research under the name of compressed sensing. In this doctoral dissertation we develop some extensions and improvements upon this technique's foundations, by modifying the random sensing matrices on which the signals of interest are projected to achieve different objectives. Firstly, we propose two methods for the adaptation of sensing matrix ensembles to the second-order moments of natural signals. These techniques leverage the maximisation of different proxies for the quantity of information acquired by compressed sensing, and are efficiently applied in the encoding of electrocardiographic tracks with minimum-complexity digital hardware. Secondly, we focus on the possibility of using compressed sensing as a method to provide a partial, yet cryptanalysis-resistant form of encryption; in this context, we show how a random matrix generation strategy with a controlled amount of perturbations can be used to distinguish between multiple user classes with different quality of access to the encrypted information content. Finally, we explore the application of compressed sensing in the design of a multispectral imager, by implementing an optical scheme that entails a coded aperture array and Fabry-Pérot spectral filters. The signal recoveries obtained by processing real-world measurements show promising results, that leave room for an improvement of the sensing matrix calibration problem in the devised imager.