Directly training spiking neural networks for cyber-physical systems: from supervised to reinforcement learning

Spiking Neural Networks (SNNs) are bio-inspired Artificial Neural Networks (ANNs) utilizing discrete spiking signals, akin to neuron communication in the brain, making them ideal for real-time and energy-efficient Cyber-Physical Systems (CPSs). This thesis explores their potential in Structural Health Monitoring (SHM), leveraging low-cost MEMS accelerometers for early damage detection in motorway bridges. The study focuses on Long Short-Term SNNs (LSNNs), although their complex learning processes pose challenges. Comparing LSNNs with other ANN models and training algorithms for SHM, findings indicate LSNNs' effectiveness in damage identification, comparable to ANNs trained using traditional methods. Additionally, an optimized embedded LSNN implementation demonstrates a 54% reduction in execution time, but with longer pre-processing due to spike-based encoding. Furthermore, SNNs are applied in UAV obstacle avoidance, trained directly using a Reinforcement Learning (RL) algorithm with event-based input from a Dynamic Vision Sensor (DVS). Performance evaluation against Convolutional Neural Networks (CNNs) highlights SNNs' superior energy efficiency, showing a 6x decrease in energy consumption. The study also investigates embedded SNN implementations' latency and throughput in real-world deployments, emphasizing their potential for energy-efficient monitoring systems. This research contributes to advancing SHM and UAV obstacle avoidance through SNNs' efficient information processing and decision-making capabilities within CPS domains.

Preliminary studies for the establishment of a tsunami early detection algorithm to be used in the frame of a Tsunami Warning System

This thesis presents and discusses TEDA, an algorithm for the automatic detection in real-time of tsunamis and large amplitude waves on sea level records. TEDA has been developed in the frame of the Tsunami Research Team of the University of Bologna for coastal tide gauges and it has been calibrated and tested for the tide gauge station of Adak Island, in Alaska. A preliminary study to apply TEDA to offshore buoys in the Pacific Ocean is also presented.

Non Invasive Tools for Early Detection of Autism Spectrum Disorders

Autism Spectrum Disorders (ASDs) describe a set of neurodevelopmental disorders. ASD represents a significant public health problem. Currently, ASDs are not diagnosed before the 2nd year of life but an early identification of ASDs would be crucial as interventions are much more effective than specific therapies starting in later childhood. To this aim, cheap an contact-less automatic approaches recently aroused great clinical interest. Among them, the cry and the movements of the newborn, both involving the central nervous system, are proposed as possible indicators of neurological disorders. This PhD work is a first step towards solving this challenging problem. An integrated system is presented enabling the recording of audio (crying) and video (movements) data of the newborn, their automatic analysis with innovative techniques for the extraction of clinically relevant parameters and their classification with data mining techniques. New robust algorithms were developed for the selection of the voiced parts of the cry signal, the estimation of acoustic parameters based on the wavelet transform and the analysis of the infant’s general movements (GMs) through a new body model for segmentation and 2D reconstruction. In addition to a thorough literature review this thesis presents the state of the art on these topics that shows that no studies exist concerning normative ranges for newborn infant cry in the first 6 months of life nor the correlation between cry and movements. Through the new automatic methods a population of control infants (“low-risk”, LR) was compared to a group of “high-risk” (HR) infants, i.e. siblings of children already diagnosed with ASD. A subset of LR infants clinically diagnosed as newborns with Typical Development (TD) and one affected by ASD were compared. The results show that the selected acoustic parameters allow good differentiation between the two groups. This result provides new perspectives both diagnostic and therapeutic.

Skin as a promising biomatrix for the early detection of misfolded proteins by RT-QuIC in neurodegenerative diseases

Real-Time Quaking-Induced Conversion (RT-QuIC) is an ultrasensitive assay capable of detecting pathological aggregates of misfolded proteins in biospecimens. In recent years, efforts have been made to find a more feasible and convenient biomatrix as an alternative to CSF, and skin biopsy may be a suitable candidate. This project aimed to evaluate the diagnostic performance of skin RT-QuIC in 3 different cohorts of patients: 1. Creutzfeldt-Jakob disease (CJD), 2. Lewy body disease (LBD), and 3. Isolated REM sleep behavior disorder (iRBD). We studied 71 punch skin samples of 35 patients with CJD, including five assessed in vitam, using 2 two different substrates: Bank vole 23-230 (Bv23-230) and Syrian hamster 23-231 (Ha23-231) recombinant prion protein. Skin prion RT-QuIC showed a 100% specificity with both substrates and a higher sensitivity with the Bv23-230 than Ha23-231 (87.5% vs. 65.6%, respectively). Forty-one patients underwent both lumbar puncture (LB) and skin biopsy; CSF and skin RT-QuIC showed a high level of concordance (38/41, 92.7%). Then, we analyzed samples taken in vitam (n=69) or postmortem (n=49) from patients with Parkinson’s disease (PD), dementia with Lewy bodies (DLB), incidental Lewy body pathology, and neurological controls. Skin α-syn RT-QuIC distinguished LBD patients with an overall accuracy of 94.1% in the two cohorts (sensitivity, 89.2%; specificity, 96.3%). Seventy-nine patients underwent both CSF and skin α-syn RT-QuIC, and the two assays yielded similar diagnostic accuracy (skin, 97.5%; CSF, 98.7%). Finally, we studied 91 iRBD patients and 41 control. In the skin, RT-QuIC showed a sensitivity of 76.9%, specificity of 97.6%, and 82.0% accuracy. 128 participants (88 patients plus 40 controls) underwent both CSF and skin RT-QuIC. The two protocols showed 99.2% of concordance. These works confirmed that skin punch biopsies might represent a valid and convenient alternative to CSF analysis for an early diagnosis of prion diseases and LB-related pathologies.

Fault detection in rotating machines by vibration signal processing techniques

Machines with moving parts give rise to vibrations and consequently noise. The setting up and the status of each machine yield to a peculiar vibration signature. Therefore, a change in the vibration signature, due to a change in the machine state, can be used to detect incipient defects before they become critical. This is the goal of condition monitoring, in which the informations obtained from a machine signature are used in order to detect faults at an early stage. There are a large number of signal processing techniques that can be used in order to extract interesting information from a measured vibration signal. This study seeks to detect rotating machine defects using a range of techniques including synchronous time averaging, Hilbert transform-based demodulation, continuous wavelet transform, Wigner-Ville distribution and spectral correlation density function. The detection and the diagnostic capability of these techniques are discussed and compared on the basis of experimental results concerning gear tooth faults, i.e. fatigue crack at the tooth root and tooth spalls of different sizes, as well as assembly faults in diesel engine. Moreover, the sensitivity to fault severity is assessed by the application of these signal processing techniques to gear tooth faults of different sizes.

A new snowfall detection algorithm for high latitude regions based on a combination of active and passive sensors

Precipitation retrieval over high latitudes, particularly snowfall retrieval over ice and snow, using satellite-based passive microwave spectrometers, is currently an unsolved problem. The challenge results from the large variability of microwave emissivity spectra for snow and ice surfaces, which can mimic, to some degree, the spectral characteristics of snowfall. This work focuses on the investigation of a new snowfall detection algorithm specific for high latitude regions, based on a combination of active and passive sensors able to discriminate between snowing and non snowing areas. The space-borne Cloud Profiling Radar (on CloudSat), the Advanced Microwave Sensor units A and B (on NOAA-16) and the infrared spectrometer MODIS (on AQUA) have been co-located for 365 days, from October 1st 2006 to September 30th, 2007. CloudSat products have been used as truth to calibrate and validate all the proposed algorithms. The methodological approach followed can be summarised into two different steps. In a first step, an empirical search for a threshold, aimed at discriminating the case of no snow, was performed, following Kongoli et al. [2003]. This single-channel approach has not produced appropriate results, a more statistically sound approach was attempted. Two different techniques, which allow to compute the probability above and below a Brightness Temperature (BT) threshold, have been used on the available data. The first technique is based upon a Logistic Distribution to represent the probability of Snow given the predictors. The second technique, defined Bayesian Multivariate Binary Predictor (BMBP), is a fully Bayesian technique not requiring any hypothesis on the shape of the probabilistic model (such as for instance the Logistic), which only requires the estimation of the BT thresholds. The results obtained show that both methods proposed are able to discriminate snowing and non snowing condition over the Polar regions with a probability of correct detection larger than 0.5, highlighting the importance of a multispectral approach.

Tecniche di progettazione tollerante alle variazioni per circuiti digitali in tecnologie nanometriche

The digital electronic market development is founded on the continuous reduction of the transistors size, to reduce area, power, cost and increase the computational performance of integrated circuits. This trend, known as technology scaling, is approaching the nanometer size. The lithographic process in the manufacturing stage is increasing its uncertainty with the scaling down of the transistors size, resulting in a larger parameter variation in future technology generations. Furthermore, the exponential relationship between the leakage current and the threshold voltage, is limiting the threshold and supply voltages scaling, increasing the power density and creating local thermal issues, such as hot spots, thermal runaway and thermal cycles. In addiction, the introduction of new materials and the smaller devices dimension are reducing transistors robustness, that combined with high temperature and frequently thermal cycles, are speeding up wear out processes. Those effects are no longer addressable only at the process level. Consequently the deep sub-micron devices will require solutions which will imply several design levels, as system and logic, and new approaches called Design For Manufacturability (DFM) and Design For Reliability. The purpose of the above approaches is to bring in the early design stages the awareness of the device reliability and manufacturability, in order to introduce logic and system able to cope with the yield and reliability loss. The ITRS roadmap suggests the following research steps to integrate the design for manufacturability and reliability in the standard CAD automated design flow: i) The implementation of new analysis algorithms able to predict the system thermal behavior with the impact to the power and speed performances. ii) High level wear out models able to predict the mean time to failure of the system (MTTF). iii) Statistical performance analysis able to predict the impact of the process variation, both random and systematic. The new analysis tools have to be developed beside new logic and system strategies to cope with the future challenges, as for instance: i) Thermal management strategy that increase the reliability and life time of the devices acting to some tunable parameter,such as supply voltage or body bias. ii) Error detection logic able to interact with compensation techniques as Adaptive Supply Voltage ASV, Adaptive Body Bias ABB and error recovering, in order to increase yield and reliability. iii) architectures that are fundamentally resistant to variability, including locally asynchronous designs, redundancy, and error correcting signal encodings (ECC). The literature already features works addressing the prediction of the MTTF, papers focusing on thermal management in the general purpose chip, and publications on statistical performance analysis. In my Phd research activity, I investigated the need for thermal management in future embedded low-power Network On Chip (NoC) devices.I developed a thermal analysis library, that has been integrated in a NoC cycle accurate simulator and in a FPGA based NoC simulator. The results have shown that an accurate layout distribution can avoid the onset of hot-spot in a NoC chip. Furthermore the application of thermal management can reduce temperature and number of thermal cycles, increasing the systemreliability. Therefore the thesis advocates the need to integrate a thermal analysis in the first design stages for embedded NoC design. Later on, I focused my research in the development of statistical process variation analysis tool that is able to address both random and systematic variations. The tool was used to analyze the impact of self-timed asynchronous logic stages in an embedded microprocessor. As results we confirmed the capability of self-timed logic to increase the manufacturability and reliability. Furthermore we used the tool to investigate the suitability of low-swing techniques in the NoC system communication under process variations. In this case We discovered the superior robustness to systematic process variation of low-swing links, which shows a good response to compensation technique as ASV and ABB. Hence low-swing is a good alternative to the standard CMOS communication for power, speed, reliability and manufacturability. In summary my work proves the advantage of integrating a statistical process variation analysis tool in the first stages of the design flow.

Nanotechnology control of self organized biomolecules and biomaterials for medical research

Nanotechnology entails the manufacturing and manipulation of matter at length scales ranging from single atoms to micron-sized objects. The ability to address properties on the biologically-relevant nanometer scale has made nanotechnology attractive for Nanomedicine. This is perceived as a great opportunity in healthcare especially in diagnostics, therapeutics and more in general to develop personalized medicine. Nanomedicine has the potential to enable early detection and prevention, and to improve diagnosis, mass screening, treatment and follow-up of many diseases. From the biological standpoint, nanomaterials match the typical size of naturally occurring functional units or components of living organisms and, for this reason, enable more effective interaction with biological systems. Nanomaterials have the potential to influence the functionality and cell fate in the regeneration of organs and tissues. To this aim, nanotechnology provides an arsenal of techniques for intervening, fabricate, and modulate the environment where cells live and function. Unconventional micro- and nano-fabrication techniques allow patterning biomolecules and biocompatible materials down to the level of a few nanometer feature size. Patterning is not simply a deterministic placement of a material; in a more extended acception it allows a controlled fabrication of structures and gradients of different nature. Gradients are emerging as one of the key factors guiding cell adhesion, proliferation, migration and even differentiation in the case of stem cells. The main goal of this thesis has been to devise a nanotechnology-based strategy and tools to spatially and temporally control biologically-relevant phenomena in-vitro which are important in some fields of medical research.

Agricultural Genetics and Plant Breeding in Early Twentieth-Century Italy

This thesis is about plant breeding in Early 20th-Century Italy. The stories of the two most prominent Italian plant-breeders of the time, Nazareno Strampelli and Francesco Todaro, are used to explore a fragment of the often-neglected history of Italian agricultural research. While Italy was not at the forefront of agricultural innovation, research programs aimed at varietal innovation did emerge in the country, along with an early diffusion of Mendelism. Using philosophical as well as historical analysis, plant breeding is analysed throughout this thesis as a process: a sequence of steps that lays on practical skills and theoretical assumptions, acting on various elements of production. Systematic plant-breeding programs in Italy started from small individual efforts, attracting more and more resources until they became a crucial part of the fascist regime's infamous agricultural policy. Hybrid varieties developed in the early 20th century survived World War II and are now ancestors of the varieties that are still cultivated today. Despite this relevance, the history of Italian wheat hybrids is today largely forgotten: this thesis is an effort to re-evaluate a part of it. The research did allow previously unknown or neglected facts to emerge, giving a new perspective on the infamous alliance between plant-breeding programs and the fascist regime. This thesis undertakes an analysis of Italian plant-breeding programs as processes. Those processes had a practical as well as a theoretical side, and involved various elements of production. Although a complete history of Italian plant breeding still remains to be written, the Italian case can now be considered along with the other case-studies that other scholars have developed in the history of plant breeding. The hope is that this historical and philosophical analysis will contribute to the on-going effort to understand the history of plants.

Hydrodynamical simulations of early-type galaxies: effects of galaxy shape and stellar dynamics on hot coronae

Early-Type galaxies (ETGs) are embedded in hot (10^6-10^7 K), X-ray emitting gaseous haloes, produced mainly by stellar winds and heated by Type Ia supernovae explosions, by the thermalization of stellar motions and occasionally by the central super-massive black hole (SMBH). In particular, the thermalization of the stellar motions is due to the interaction between the stellar and the SNIa ejecta and the hot interstellar medium (ISM) already residing in the ETG. A number of different astrophysical phenomena determine the X-ray properties of the hot ISM, such as stellar population formation and evolution, galaxy structure and internal kinematics, Active Galactic Nuclei (AGN) presence, and environmental effects. With the aid of high-resolution hydrodynamical simulations performed on state-of-the-art galaxy models, in this Thesis we focus on the effects of galaxy shape, stellar kinematics and star formation on the evolution of the X-ray coronae of ETGs. Numerical simulations show that the relative importance of flattening and rotation are functions of the galaxy mass: at low galaxy masses, adding flattening and rotation induces a galactic wind, thus lowering the X-ray luminosity; at high galaxy masses the angular momentum conservation keeps the central regions of rotating galaxies at low density, whereas in non-rotating models a denser and brighter atmosphere is formed. The same dependence from the galaxy mass is present in the effects of star formation (SF): in light galaxies SF contributes to increase the spread in Lx, while at high galaxy masses the halo X-ray properties are marginally sensitive to SF effects. In every case, the star formation rate at the present epoch quite agrees with observations, and the massive, cold gaseous discs are partially or completely consumed by SF on a time-scale of few Gyr, excluding the presence of young stellar discs at the present epoch.

Diagnosis and Fault detection in Electrical Machines and Drives based on Advanced Signal Processing Techniques

In the present thesis, a new methodology of diagnosis based on advanced use of time-frequency technique analysis is presented. More precisely, a new fault index that allows tracking individual fault components in a single frequency band is defined. More in detail, a frequency sliding is applied to the signals being analyzed (currents, voltages, vibration signals), so that each single fault frequency component is shifted into a prefixed single frequency band. Then, the discrete Wavelet Transform is applied to the resulting signal to extract the fault signature in the frequency band that has been chosen. Once the state of the machine has been qualitatively diagnosed, a quantitative evaluation of the fault degree is necessary. For this purpose, a fault index based on the energy calculation of approximation and/or detail signals resulting from wavelet decomposition has been introduced to quantify the fault extend. The main advantages of the developed new method over existing Diagnosis techniques are the following: - Capability of monitoring the fault evolution continuously over time under any transient operating condition; - Speed/slip measurement or estimation is not required; - Higher accuracy in filtering frequency components around the fundamental in case of rotor faults; - Reduction in the likelihood of false indications by avoiding confusion with other fault harmonics (the contribution of the most relevant fault frequency components under speed-varying conditions are clamped in a single frequency band); - Low memory requirement due to low sampling frequency; - Reduction in the latency of time processing (no requirement of repeated sampling operation).

Early Warning For Large Earthquakes: Observations, Models and Real-Time Data Analysis

This thesis is a collection of works focused on the topic of Earthquake Early Warning, with a special attention to large magnitude events. The topic is addressed from different points of view and the structure of the thesis reflects the variety of the aspects which have been analyzed. The first part is dedicated to the giant, 2011 Tohoku-Oki earthquake. The main features of the rupture process are first discussed. The earthquake is then used as a case study to test the feasibility Early Warning methodologies for very large events. Limitations of the standard approaches for large events arise in this chapter. The difficulties are related to the real-time magnitude estimate from the first few seconds of recorded signal. An evolutionary strategy for the real-time magnitude estimate is proposed and applied to the single Tohoku-Oki earthquake. In the second part of the thesis a larger number of earthquakes is analyzed, including small, moderate and large events. Starting from the measurement of two Early Warning parameters, the behavior of small and large earthquakes in the initial portion of recorded signals is investigated. The aim is to understand whether small and large earthquakes can be distinguished from the initial stage of their rupture process. A physical model and a plausible interpretation to justify the observations are proposed. The third part of the thesis is focused on practical, real-time approaches for the rapid identification of the potentially damaged zone during a seismic event. Two different approaches for the rapid prediction of the damage area are proposed and tested. The first one is a threshold-based method which uses traditional seismic data. Then an innovative approach using continuous, GPS data is explored. Both strategies improve the prediction of large scale effects of strong earthquakes.

Relationships between plant diversity and environmental heterogeneity in rupicolous grasslands on gypsum. The case study of Alysso-Sedion albi (Habitat 6110).

Plant communities on weathered rock and outcrops are characterized by high values in species richness (Dengler 2006) and often persist on small and fragmented surfaces. Yet very few studies have examined the relationships between heterogeneity and plant diversity at small scales, in particular in poor-nutrient and low productive environment (Shmida and Wilson 1985, Lundholm 2003). In order to assess these relationships both in space and time in relationship, two different approaches were employed in the present study, in two gypsum outcrops of Northern Apennine. Diachronic and synchronic samplings from April 2012 to March 2013 were performed. A 50x50 cm plot was used in both samplings such as the sampling unit base. The diachronic survey aims to investigate seasonal patterning of plant diversity by the use of images analysis techniques integrated with field data and considering also seasonal climatic trend, the substrate quality and its variation in time. The purpose of the further, synchronic sampling was to describe plant diversity pattern as a function of the environmental heterogeneity meaning in substrate typologies, soil depth and topographic features. Results showed that responses of diversity pattern depend both on the resources availability, environmental heterogeneity and the manner in which the different taxonomic group access to them during the year. Species richness and Shannon diversity were positively affected by increasing in substrate heterogeneity. Furthermore a good turnover in seasonal species occurrence was detected. This vegetation may be described by the coexistence of three groups of species which created a gradient from early colonization stages, characterized by greater slope and predominance of bare rock, gradually to situation of more developed soil.

Fault detection, diagnosis and active fault tolerant control for a satellite attitude control system

Modern control systems are becoming more and more complex and control algorithms more and more sophisticated. Consequently, Fault Detection and Diagnosis (FDD) and Fault Tolerant Control (FTC) have gained central importance over the past decades, due to the increasing requirements of availability, cost efficiency, reliability and operating safety. This thesis deals with the FDD and FTC problems in a spacecraft Attitude Determination and Control System (ADCS). Firstly, the detailed nonlinear models of the spacecraft attitude dynamics and kinematics are described, along with the dynamic models of the actuators and main external disturbance sources. The considered ADCS is composed of an array of four redundant reaction wheels. A set of sensors provides satellite angular velocity, attitude and flywheel spin rate information. Then, general overviews of the Fault Detection and Isolation (FDI), Fault Estimation (FE) and Fault Tolerant Control (FTC) problems are presented, and the design and implementation of a novel diagnosis system is described. The system consists of a FDI module composed of properly organized model-based residual filters, exploiting the available input and output information for the detection and localization of an occurred fault. A proper fault mapping procedure and the nonlinear geometric approach are exploited to design residual filters explicitly decoupled from the external aerodynamic disturbance and sensitive to specific sets of faults. The subsequent use of suitable adaptive FE algorithms, based on the exploitation of radial basis function neural networks, allows to obtain accurate fault estimations. Finally, this estimation is actively exploited in a FTC scheme to achieve a suitable fault accommodation and guarantee the desired control performances. A standard sliding mode controller is implemented for attitude stabilization and control. Several simulation results are given to highlight the performances of the overall designed system in case of different types of faults affecting the ADCS actuators and sensors.

Relevance of the pig stomach for the detection of dietary factors and gut maturation and control

In this thesis two approaches were applied to achieve a double general objective. The first chapter was dedicated to the study of the distribution of the expression of genes of several bitter and fat receptor in several gastrointestinal tracts. A set of 7 genes for bitter taste and for 3 genes for fat taste was amplified with real-time PCR from mRNA extracted from 5 gastrointestinal segments of weaned pigs. The presence of gene expression for several chemosensing receptors for bitter and fat taste in different compartments of the stomach confirms that this organ should be considered a player for the early detection of bolus composition. In the second chapter we investigated in young pigs the distribution of butyrate-sensing olfactory receptor (OR51E1) receptor along the GIT, its relation with some endocrine markers, its variation with age, and after interventions affecting the gut environment and intestinal microbiota in piglets and in different tissues. Our results indicate that OR51E1 is strictly related to the normal GIT enteroendocrine activity. In the third chapter we investigated the differential gene expression between oxyntic and pyloric mucosa in seven starter pigs. The obtained data indicate that there is significant differential gene exression between oxintic of the young pig and pyloric mucosa and further functional studies are needed to confirm their physiological importance. In the last chapter, thymol, that has been proposed as an oral alternative to antibiotics in the feed of pigs and broilers, was introduced directly into the stomach of 8 weaned pigs and sampled for gastric oxyntic and pyloric mucosa. The analysis of the whole transcript expression shoes that the stimulation of gastric proliferative activity and the control of digestive activity by thymol can influence positively gastric maturation and function in the weaned pigs.