Paleosol-based stratigraphy of late Quaternary deposits from the Po basin, between Bologna and the Po river (Northern Italy)

Stratigraphic studies carried out over the last decades in Italy and elsewhere testify a growing interest in Quaternary deposits and in the influence of climate change on their architecture. The subsurface of the Po Plain, in its topmost portion, is made up of alluvial deposits organized in depositional cycles at different scales. This PhD thesis provides millennial-scale stratigraphic reconstruction of the Late Pleistocene-Holocene deposits beneath the southern Po Plain, based on basin-scale correlation of laterally-extensive buried soil horizons. Far from the aim of characterizing palaeosols from a mineralogical and geochemical point of view, we focused on the physical and stratigraphic significance of these horizons. In the Bologna urban area, which hosts an abundance of stratigraphic data, the correlation between seventeen continuously-cored boreholes led to the identification of five vertically-stacked palaeosol-bounded sequences within the 14C time window. In a wide portion of the alluvial plain north of Bologna, far away from the Apenninic margin and from the Po River, where subsurface stratigraphic architecture is dominated by markedly lenticular sediment bodies, palaeosols revealed to be the only stratigraphic marker of remarkable lateral continuity. These horizons are characterized by peculiar resistance values, which make them easily identifiable via pocket penetration tests. Palaeosols reveal specific geometric relationships with the associated alluvial facies associations, allowing reliable estimates of soil development as a function of alluvial dynamics. With the aid of sixty new radiocarbon dates, a reliable age attribution and likely time intervals of exposure were assigned to each palaeosol. Vertically-stacked palaeosols delimitate short-term depositional cycles, likely related to the major episodes of climatic change of the last 40 ky. Through integration of stratigraphic data with 750 archaeological reports from the Bologna area, the impact of human settlements on depositional and pedogenic processes during the late Holocene was investigated.

Mental states monitoring through passive Brain-Computer Interface systems

The monitoring of cognitive functions aims at gaining information about the current cognitive state of the user by decoding brain signals. In recent years, this approach allowed to acquire valuable information about the cognitive aspects regarding the interaction of humans with external world. From this consideration, researchers started to consider passive application of brain–computer interface (BCI) in order to provide a novel input modality for technical systems solely based on brain activity. The objective of this thesis is to demonstrate how the passive Brain Computer Interfaces (BCIs) applications can be used to assess the mental states of the users, in order to improve the human machine interaction. Two main studies has been proposed. The first one allows to investigate whatever the Event Related Potentials (ERPs) morphological variations can be used to predict the users’ mental states (e.g. attentional resources, mental workload) during different reactive BCI tasks (e.g. P300-based BCIs), and if these information can predict the subjects’ performance in performing the tasks. In the second study, a passive BCI system able to online estimate the mental workload of the user by relying on the combination of the EEG and the ECG biosignals has been proposed. The latter study has been performed by simulating an operative scenario, in which the occurrence of errors or lack of performance could have significant consequences. The results showed that the proposed system is able to estimate online the mental workload of the subjects discriminating three different difficulty level of the tasks ensuring a high reliability.

Sviluppo del software di un sistema per l'analisi in tempo reale della combustione di motori endotermici alternativi

Limiti sempre più stringenti sulle emissioni inquinanti ed una maggiore attenzione ai consumi, all'incremento di prestazioni e alla guidabilità, portano allo sviluppo di algoritmi di controllo motore sempre più complicati. Allo stesso tempo, l'unità di propulsione sta diventando un insieme sempre più variegato di sottosistemi che devono lavorare all'unisono. L'ingegnere calibratore si trova di fronte ad una moltitudine di variabili ed algoritmi che devono essere calibrati e testati e necessita di strumenti che lo aiutino ad analizzare il comportamento del motore fornendo risultati sintetici e facilmente accessibili. Nel seguente lavoro è riportato lo sviluppo di un sistema di analisi della combustione: l'obbiettivo è stato quello di sviluppare un software che fornisca le migliori soluzioni per l'analisi di un motore a combustione interna, in termini di accuratezza dei risultati, varietà di calcoli messi a disposizione, facilità di utilizzo ed integrazione con altri sistemi tramite la condivisione dei risultati calcolati in tempo reale.

Tensor-Train decomposition for image classification problems

In these last years a great effort has been put in the development of new techniques for automatic object classification, also due to the consequences in many applications such as medical imaging or driverless cars. To this end, several mathematical models have been developed from logistic regression to neural networks. A crucial aspect of these so called classification algorithms is the use of algebraic tools to represent and approximate the input data. In this thesis, we examine two different models for image classification based on a particular tensor decomposition named Tensor-Train (TT) decomposition. The use of tensor approaches preserves the multidimensional structure of the data and the neighboring relations among pixels. Furthermore the Tensor-Train, differently from other tensor decompositions, does not suffer from the curse of dimensionality making it an extremely powerful strategy when dealing with high-dimensional data. It also allows data compression when combined with truncation strategies that reduce memory requirements without spoiling classification performance. The first model we propose is based on a direct decomposition of the database by means of the TT decomposition to find basis vectors used to classify a new object. The second model is a tensor dictionary learning model, based on the TT decomposition where the terms of the decomposition are estimated using a proximal alternating linearized minimization algorithm with a spectral stepsize.

Big data analytics for proactive and predictive maintenance in electric car battery packs

The idea behind the project is to develop a methodology for analyzing and developing techniques for the diagnosis and the prediction of the state of charge and health of lithium-ion batteries for automotive applications. For lithium-ion batteries, residual functionality is measured in terms of state of health; however, this value cannot be directly associated with a measurable value, so it must be estimated. The development of the algorithms is based on the identification of the causes of battery degradation, in order to model and predict the trend. Therefore, models have been developed that are able to predict the electrical, thermal and aging behavior. In addition to the model, it was necessary to develop algorithms capable of monitoring the state of the battery, online and offline. This was possible with the use of algorithms based on Kalman filters, which allow the estimation of the system status in real time. Through machine learning algorithms, which allow offline analysis of battery deterioration using a statistical approach, it is possible to analyze information from the entire fleet of vehicles. Both systems work in synergy in order to achieve the best performance. Validation was performed with laboratory tests on different batteries and under different conditions. The development of the model allowed to reduce the time of the experimental tests. Some specific phenomena were tested in the laboratory, and the other cases were artificially generated.

A robotic platform for precision agriculture and applications

Agricultural techniques have been improved over the centuries to match with the growing demand of an increase in global population. Farming applications are facing new challenges to satisfy global needs and the recent technology advancements in terms of robotic platforms can be exploited. As the orchard management is one of the most challenging applications because of its tree structure and the required interaction with the environment, it was targeted also by the University of Bologna research group to provide a customized solution addressing new concept for agricultural vehicles. The result of this research has blossomed into a new lightweight tracked vehicle capable of performing autonomous navigation both in the open-filed scenario and while travelling inside orchards for what has been called in-row navigation. The mechanical design concept, together with customized software implementation has been detailed to highlight the strengths of the platform and some further improvements envisioned to improve the overall performances. Static stability testing has proved that the vehicle can withstand steep slopes scenarios. Some improvements have also been investigated to refine the estimation of the slippage that occurs during turning maneuvers and that is typical of skid-steering tracked vehicles. The software architecture has been implemented using the Robot Operating System (ROS) framework, so to exploit community available packages related to common and basic functions, such as sensor interfaces, while allowing dedicated custom implementation of the navigation algorithm developed. Real-world testing inside the university’s experimental orchards have proven the robustness and stability of the solution with more than 800 hours of fieldwork. The vehicle has also enabled a wide range of autonomous tasks such as spraying, mowing, and on-the-field data collection capabilities. The latter can be exploited to automatically estimate relevant orchard properties such as fruit counting and sizing, canopy properties estimation, and autonomous fruit harvesting with post-harvesting estimations.