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.

Intelligent Sensor Systems for Structural Health Monitoring Applications

The convergence between the recent developments in sensing technologies, data science, signal processing and advanced modelling has fostered a new paradigm to the Structural Health Monitoring (SHM) of engineered structures, which is the one based on intelligent sensors, i.e., embedded devices capable of stream processing data and/or performing structural inference in a self-contained and near-sensor manner. To efficiently exploit these intelligent sensor units for full-scale structural assessment, a joint effort is required to deal with instrumental aspects related to signal acquisition, conditioning and digitalization, and those pertaining to data management, data analytics and information sharing. In this framework, the main goal of this Thesis is to tackle the multi-faceted nature of the monitoring process, via a full-scale optimization of the hardware and software resources involved by the {SHM} system. The pursuit of this objective has required the investigation of both: i) transversal aspects common to multiple application domains at different abstraction levels (such as knowledge distillation, networking solutions, microsystem {HW} architectures), and ii) the specificities of the monitoring methodologies (vibrations, guided waves, acoustic emission monitoring). The key tools adopted in the proposed monitoring frameworks belong to the embedded signal processing field: namely, graph signal processing, compressed sensing, ARMA System Identification, digital data communication and TinyML.

Studio degli effetti di citochine a basso dosaggio (LOW DOSE) su cellule di origine animale (3T3-L1) ed umana (hASC)

Nella sindrome metabolica l’insulino-resistenza e l’obesità rappresentano i fattori chiave nello sviluppo di tale patologia, ma il principale player risulta un’infiammazione cronica di basso grado (Chronic Low Grade Inflammation) a carico del tessuto adiposo. Lo scopo di questo progetto di ricerca è quindi stato quello di testare citochine a basso dosaggio come possibile trattamento dell’infiammazione cronica. Le citochine utilizzate (GUNA®-Interleukin 4 (IL-4), GUNA®-Interleukin 10 (IL-10), GUNA®-Melatonin, GUNA®-Melatonin+GUNA®-IL-4.) sono state fornite dall’azienda GUNA S.p.a. Poiché l’infiammazione cronica a basso grado inizia in seguito ad un aumento eccessivo del tessuto adiposo, inizialmente si è valutato l’effetto su una linea di preadipociti murini (3T3-L1). Questa prima parte dello studio ha messo in evidenza come le citochine a basso dosaggio non modificano la vitalità cellulare, anche se agiscono sull’espressione e la localizzazione di vimentina e E-caderina. Inoltre IL-4 e IL-10 sembrano avere una parziale attività inibitoria, non significativa, sull’adipogenesi ad eccezione dell’espressione dell’adiponectina che appare significativamente aumentata. In ultimo i trattamenti con IL-4 e IL-10 hanno mostrato una diminuzione del contenuto di ROS e una ridotta attività antiinfiammatoria dovuta alla diminuzione di IL-6 secreto. Un’altra popolazione cellulare principale nel tessuto adiposo è rappresentata dalle ASC (Adipose Stem Cell). Per tale motivo si è proseguito valutando l’effetto che le citochine low-dose su questo citotipo, evidenziando che il trattamento con le citochine non risulta essere tossico, anche se sembrerebbe rallentare la crescita cellulare, e determina un’inibizione del processo adipogenico. Inoltre il trattamento con IL-10 sembra stimolare le ASC a produrre fattori che inducono una maggiore vasculogenesi e le induce a produrre fattori chemiotattici che determinano una maggiore capacità di rigenerazione tissutale da parte di MSC da derma. Infine, il trattamento con IL-4 e IL-10 stimola probabilmente una minore produzione di citochine pro-infiammatorie che inducono in maniera significativa una minore mobilità di cellule MSC.

Antipodal random sequences with prescribed second-order statistics: application to Compressive Sensing and UWB system based on DS-CDMA

It is usual to hear a strange short sentence: «Random is better than...». Why is randomness a good solution to a certain engineering problem? There are many possible answers, and all of them are related to the considered topic. In this thesis I will discuss about two crucial topics that take advantage by randomizing some waveforms involved in signals manipulations. In particular, advantages are guaranteed by shaping the second order statistic of antipodal sequences involved in an intermediate signal processing stages. The first topic is in the area of analog-to-digital conversion, and it is named Compressive Sensing (CS). CS is a novel paradigm in signal processing that tries to merge signal acquisition and compression at the same time. Consequently it allows to direct acquire a signal in a compressed form. In this thesis, after an ample description of the CS methodology and its related architectures, I will present a new approach that tries to achieve high compression by design the second order statistics of a set of additional waveforms involved in the signal acquisition/compression stage. The second topic addressed in this thesis is in the area of communication system, in particular I focused the attention on ultra-wideband (UWB) systems. An option to produce and decode UWB signals is direct-sequence spreading with multiple access based on code division (DS-CDMA). Focusing on this methodology, I will address the coexistence of a DS-CDMA system with a narrowband interferer. To do so, I minimize the joint effect of both multiple access (MAI) and narrowband (NBI) interference on a simple matched filter receiver. I will show that, when spreading sequence statistical properties are suitably designed, performance improvements are possible with respect to a system exploiting chaos-based sequences minimizing MAI only.