Private sensors and crowdsourced rainfall data: accuracy and effectiveness for monitoring severe events and mapping pluvial flooding in urban areas

The increasing number of extreme rainfall events, combined with the high population density and the imperviousness of the land surface, makes urban areas particularly vulnerable to pluvial flooding. In order to design and manage cities to be able to deal with this issue, the reconstruction of weather phenomena is essential. Among the most interesting data sources which show great potential are the observational networks of private sensors managed by citizens (crowdsourcing). The number of these personal weather stations is consistently increasing, and the spatial distribution roughly follows population density. Precisely for this reason, they perfectly suit this detailed study on the modelling of pluvial flood in urban environments. The uncertainty associated with these measurements of precipitation is still a matter of research. In order to characterise the accuracy and precision of the crowdsourced data, we carried out exploratory data analyses. A comparison between Netatmo hourly precipitation amounts and observations of the same quantity from weather stations managed by national weather services is presented. The crowdsourced stations have very good skills in rain detection but tend to underestimate the reference value. In detail, the accuracy and precision of crowd- sourced data change as precipitation increases, improving the spread going to the extreme values. Then, the ability of this kind of observation to improve the prediction of pluvial flooding is tested. To this aim, the simplified raster-based inundation model incorporated in the Saferplaces web platform is used for simulating pluvial flooding. Different precipitation fields have been produced and tested as input in the model. Two different case studies are analysed over the most densely populated Norwegian city: Oslo. The crowdsourced weather station observations, bias-corrected (i.e. increased by 25%), showed very good skills in detecting flooded areas.

Augmented Reality for Airport Control Tower: Analysis and Implementation of Safety Nets

With the development of new technologies, Air Traffic Control, in the nearby of the airport, switched from a purely visual control to the use of radar, sensors and so on. As the industry is switching to the so-called Industry 4.0, also in this frame, it would be possible to implement some of the new tools that can facilitate the work of Air Traffic Controllers. The European Union proposed an innovative project to help the digitalization of the European Sky by means of the Single European Sky ATM Research (SESAR) program, which is the foundation on which the Single European Sky (SES) is based, in order to improve the already existing technologies to transform Air Traffic Management in Europe. Within this frame, the Resilient Synthetic Vision for Advanced Control Tower Air Navigation Service Provision (RETINA) project, which saw the light in 2016, studied the possibility to apply new tools within the conventional control tower to reduce the air traffic controller workload, thanks to the improvements in the augmented reality technologies. After the validation of RETINA, the Digital Technologies for Tower (DTT) project was established and the solution proposed by the University of Bologna aimed, among other things, to introduce Safety Nets in a Head-Up visualization. The aim of this thesis is to analyze the Safety Nets in use within the control tower and, by developing a working concept, implement them in a Head-Up view to be tested by Air Traffic Control Operators (ATCOs). The results, coming from the technical test, show that this concept is working and it could be leading to a future implementation in a real environment, as it improves the air traffic controller working conditions also when low visibility conditions apply.

Structure learning of graphical models for task-oriented robot grasping

In the collective imaginaries a robot is a human like machine as any androids in science fiction. However the type of robots that you will encounter most frequently are machinery that do work that is too dangerous, boring or onerous. Most of the robots in the world are of this type. They can be found in auto, medical, manufacturing and space industries. Therefore a robot is a system that contains sensors, control systems, manipulators, power supplies and software all working together to perform a task. The development and use of such a system is an active area of research and one of the main problems is the development of interaction skills with the surrounding environment, which include the ability to grasp objects. To perform this task the robot needs to sense the environment and acquire the object informations, physical attributes that may influence a grasp. Humans can solve this grasping problem easily due to their past experiences, that is why many researchers are approaching it from a machine learning perspective finding grasp of an object using information of already known objects. But humans can select the best grasp amongst a vast repertoire not only considering the physical attributes of the object to grasp but even to obtain a certain effect. This is why in our case the study in the area of robot manipulation is focused on grasping and integrating symbolic tasks with data gained through sensors. The learning model is based on Bayesian Network to encode the statistical dependencies between the data collected by the sensors and the symbolic task. This data representation has several advantages. It allows to take into account the uncertainty of the real world, allowing to deal with sensor noise, encodes notion of causality and provides an unified network for learning. Since the network is actually implemented and based on the human expert knowledge, it is very interesting to implement an automated method to learn the structure as in the future more tasks and object features can be introduced and a complex network design based only on human expert knowledge can become unreliable. Since structure learning algorithms presents some weaknesses, the goal of this thesis is to analyze real data used in the network modeled by the human expert, implement a feasible structure learning approach and compare the results with the network designed by the expert in order to possibly enhance it.

Stabilizzazione del Blu di Prussia con film politiofenici depositati per via elettrochimica

Hydrogen peroxide (H2O2) is a powerful oxidant which is commonly used in a wide range of applications in the industrial field. Several methods for the quantification of H2O2 have been developed. Among them, electrochemical methods exploit the ability of some hexacyanoferrates (such as Prussian Blue) to detect H2O2 at potentials close to 0.0 V (vs. SCE) avoiding the occurrence of secondary reactions, which are likely to run at large overpotentials. This electrocatalytic behaviour makes hexacyanoferrates excellent redox mediators. When deposited in the form of thin films on the electrode surfaces, they can be employed in the fabrication of sensors and biosensors, normally operated in solutions at pH values close to physiological ones. As hexacyanoferrates show limited stability in not strongly acidic solutions, it is necessary to improve the configuration of the modified electrodes to increase the stability of the films. In this thesis work, organic conducting polymers were used to fabricate composite films with Prussian Blue (PB) to be electro-deposited on Pt surfaces, in order to increase their pH stability. Different electrode configurations and different methods of synthesis of both components were tested, and for each one the achievement of a possible increase in the operational stability of Prussian Blue was verified. Good results were obtained for the polymer 3,3''-didodecyl-2,2':5',2''-terthiophene (poly(3,3''-DDTT)), whose presence created a favourable microenvironment for the electrodeposition of Prussian Blue. The electrochemical behaviour of the modified electrodes was studied in both aqueous and organic solutions. Poly(3,3''-DDTT) showed no response in aqueous solution in the potential range where PB is electroactive, thus in buffered aqueous solution is was possible to characterize the composite material, focusing only on the redox behaviour of PB. A combined effect of anion and cation of the supporting electrolyte was noticed. The response of Pt electrodes modified with films of the PB /poly(3,3''-DDTT) composite was evaluated for the determination of H2O2. The performance of such films was found better than that of the PB alone. It can be concluded that poly(3,3''-DDTT) plays a key role in the stabilization of Prussian Blue causing also a wider linearity range for the electrocatalytic response to H2O2.

Digital design of an EPC Gen2 controller for enhanced RFID tags

This thesis presents an improvement of the long range battery-less UHF RFID platform for sensor applications which is based on the open source Wireless Identification and Sensing Platform (WISP) project. The purpose of this work is to design a digital logic that performs the RFID EPC gen2 protocol communication, is able to acquire information by sensors and provide an accurate estimation of tag location ensuring low energy consumption. This thesis will describe the hardware architecture on which the digital logic was inserted, the Verilog code developed, the methods by which the digital logic was tested and an explorative study of chip synthesis on Cadence.

Sensori elettrochimici a base di PEDOT:PSS stampati a getto di inchiostro

Conductive polymers (CPS) are a class of carbon-based materials, capable of conducting electric current, characterized by metallic properties in combination with the intrinsic properties of conventional polymers. The structural model of the CP consists of a system of double π-conjugated on the backbone (polyene structure) which can easily undergo reversible doping reaching a wide range of conductivity. Thanks to their versatility and peculiar properties (mechanical flexibility, biocompatibility, transparency, ease of chemical functionalization, high thermal stability), CPS have revolutionized the science of materials giving rise to Organic Bioelectronics, the discipline resulting from the convergence between biology and electronics. The Poly (3,4-ethylenedioxythiophene) : poly (styrenesulfonate) (PEDOT: PSS), complex polyelectrolyte, in the form of a thin film, currently represents the reference standard in applications concerning Bioelectronics. In this project, two types of electrochemical sensors ink-jet printed on a flexible polymeric substrate, the polyethylene terephthalate, have been developed and characterized. The Drop on Demand (DOD) inkjet technology has allowed to control the positioning of fluid volumes of the order of picoliters with an accuracy of ± 25μm. This resulted in the creation of amperometric sensors and organic electrochemical transistors (OECT) all-PEDOT: PSS with high reproducibility. The sensors have been used for the determination of Ascorbic Acid (AA) which is currently considered an important benchmark in the field of sensors. In Cyclic Voltammetry, the amperometric sensor has detected AA at potentials less than 0.2 V vs. SCE thanks to the electrocatalytic properties of the PEDOT: PSS. On the other hand, the OECT detected AA concentrations equal to 10 nanomolar in Chronoamperometry. Furthermore, a promising new generation of all-printed OECTS, consisting of silver metal contacts, has been created. Preliminary results are presented.

Study of risky situations using experimental data coming from an instrumented bicycle

The aim of this master’s thesis is to study the risky situations of the cyclist when they interact with road infrastructure and other road users as well as the influence of speed on safety. This research activity is linked with the SAFERUP (Sustainable, Accessible, Resilient, and Smart Urban Pavement) European funded project where one of the doctoral candidate has performed experiments on the bicycle simulation at the Gustave Eiffel university in the PICS-L laboratory (Paris) and instrumented bicycle at the Stockholm (Sweden). The approach of the experiment was to hire a number of people who have participated in the riding of the Instrumented bicycle (Stockholm) and bicycle simulator (PICS-L) which were developed by attaching different sensors and devices to measure important parameters of the bicycle riding and their data was collected to analysis in order to understand the behavior of the cyclist to improve the safety. In addition, a mobile eye tracker wore by participants to record the real experiment scenario, and after the end of the trip, each participant shared their remarks regarding their experience of bicycle riding according to different portions of the road infrastructure. In this research main focus is to analyze the relevant data such as speed profiles, video recordings and questionnaire surveys from the instrumented bicycle experiment. In fact, critical situations, where there was a higher probability, were compared with the subjective evaluation of the participant to be conscious of the issues related to the safety and comfort of the cyclist in different road characteristics.