Implementation of network entropy algorithms on hpc machines, with application to high-dimensional experimental data

Network Theory is a prolific and lively field, especially when it approaches Biology. New concepts from this theory find application in areas where extensive datasets are already available for analysis, without the need to invest money to collect them. The only tools that are necessary to accomplish an analysis are easily accessible: a computing machine and a good algorithm. As these two tools progress, thanks to technology advancement and human efforts, wider and wider datasets can be analysed. The aim of this paper is twofold. Firstly, to provide an overview of one of these concepts, which originates at the meeting point between Network Theory and Statistical Mechanics: the entropy of a network ensemble. This quantity has been described from different angles in the literature. Our approach tries to be a synthesis of the different points of view. The second part of the work is devoted to presenting a parallel algorithm that can evaluate this quantity over an extensive dataset. Eventually, the algorithm will also be used to analyse high-throughput data coming from biology.

Compliance verification of virtual Phasor Measuring Unit using Ip-DFT algorithm according to the IEEE-60255-118-1(2018).

With the increase in load demand for various sectors, protection and safety of the network are key factors that have to be taken into consideration over the electric grid and distribution network. A phasor Measuring unit is an Intelligent electronics device that collects the data in the form of a real-time synchrophasor with a precise time tag using GPS (Global positioning system) and transfers the data to the grid command to monitor and assess the data. The measurements made by PMU have to be very precise to protect the relays and measuring equipment according to the IEEE 60255-118-1(2018). As a device PMU is very expensive to research and develop new functionalities there is a need to find an alternative to working with. Hence many open source virtual libraries are available to replicate the exact function of PMU in the virtual environment(Software) to continue the research on multiple objectives, providing the very least error results when verified. In this thesis, I executed performance and compliance verification of the virtual PMU which was developed using the I-DFT (Interpolated Discrete Fourier transforms) C-class algorithm in MATLAB. In this thesis, a test environment has been developed in MATLAB and tested the virtually developed PMU on both steady state and dynamic state for verifying the latest standard compliance(IEEE-60255-118-1).

Analysis of fragmentation cross sections of GSI 2021 data for the FOOT experiment

Hadrontherapy is a medical treatment based on the use of charged particles beams accelerated towards deep-seated tumors on clinical patients. The reason why it is increasingly used is the favorable depth dose profile following the Bragg Peak distribution, where the release of dose is almost sharply focused near the end of the beam path. However, nuclear interactions between the beam and the human body constituents occur, generating nuclear fragments which modify the dose profile. To overcome the lack of experimental data on nuclear fragmentation reactions in the energy range of hadrontherapy interest, the FOOT (FragmentatiOn Of Target) experiment has been conceived with the main aim of measuring differential nuclear fragmentation cross sections with an uncertainty lower than 5\%. The same results are of great interest also in the radioprotection field, studying similar processes. Long-term human missions outside the Earth’s orbit are going to be planned in the next years, among which the NASA foreseen travel to Mars, and it is fundamental to protect astronauts health and electronics from radiation exposure .\\ In this thesis, a first analysis of the data taken at the GSI with a beam of $^{16}O$ at 400 $MeV/u$ impinging on a target of graphite ($C$) will be presented, showing the first preliminary results of elemental cross section and angular differential cross section. A Monte Carlo dataset was first studied to test the performance of the tracking reconstruction algorithm and to check the reliability of the full analysis chain, from hit reconstruction to cross section measurement. An high agreement was found between generated and reconstructed fragments, thus validating the adopted procedure. A preliminary experimental cross section was measured and compared with MC results, highlighting a good consistency for all the fragments.

Machine Learning algorithm on seat comfort level measurement, mapping pressure values with body regions

The comfort level of the seat has a major effect on the usage of a vehicle; thus, car manufacturers have been working on elevating car seat comfort as much as possible. However, still, the testing and evaluation of comfort are done using exhaustive trial and error testing and evaluation of data. In this thesis, we resort to machine learning and Artificial Neural Networks (ANN) to develop a fully automated approach. Even though this approach has its advantages in minimizing time and using a large set of data, it takes away the degree of freedom of the engineer on making decisions. The focus of this study is on filling the gap in a two-step comfort level evaluation which used pressure mapping with body regions to evaluate the average pressure supported by specific body parts and the Self-Assessment Exam (SAE) questions on evaluation of the person’s interest. This study has created a machine learning algorithm that works on giving a degree of freedom to the engineer in making a decision when mapping pressure values with body regions using ANN. The mapping is done with 92% accuracy and with the help of a Graphical User Interface (GUI) that facilitates the process during the testing time of comfort level evaluation of the car seat, which decreases the duration of the test analysis from days to hours.

Spacecraft Collision Probability Calculation from Conjunction Data Message and High Precision Orbit Propagator

The increasing number of Resident Space Objects (RSOs) is a threat to spaceflight operations. Conjunction Data Messages (CDMs) are sent to satellite operators to warn for possible future collision and their probabilities. The research project described herein pushed forward an algorithm that is able to update the collision probability directly on-board starting from CDMs and the state vector of the hosting satellite which is constantly updated thanks to an onboard GNSS receiver. A large set of methods for computing the collision probability was analyzed in order to find the best ones for this application. The selected algorithm was then tested to assess and improve its performance. Finally, parts of the algorithm and external software were implemented on a Raspberry Pi 3B+ board to demonstrate the compatibility of this approach with computational resources similar to those typically available onboard modern spacecraft.

Towards a Smart Campus Digital Twin: Promoting Awareness and Sustainability Through Wayfinding and Real-Time Environmental Data

The newly inaugurated Navile District of the University of Bologna is a complex created along the Navile canal, that now houses various teaching and research activities for the disciplines of Chemistry, Industrial Chemistry, Pharmacy, Biotechnology and Astronomy. A Building Information Modeling system (BIM) gives staff of the Navile campus several ways to monitor buildings in the complex throughout their life cycle, one of which is the ability to access real-time environmental data such as room temperature, humidity, air composition, and more, thereby simplifying operations like finding faults and optimizing environmental resource usage. But smart features at Navile are not only available to the staff: AlmaMap Navile is a web application, whose development is documented in this thesis, that powers the public touch kiosks available throughout the campus, offering maps of the district and indications on how to reach buildings and spaces. Even if these two systems, BIM and AlmaMap, don't seem to have many similarities, they share the common intent of promoting awareness for informed decision making in the campus, and they do it while relying on web standards for communication. This opens up interesting possibilities, and is the idea behind AlmaMap Navile 2.0, an app that interfaces with the BIM system and combines real-time sensor data with a comfort calculation algorithm, giving users the ability not just to ask for directions to a space, but also to see its comfort level in advance and, should they want to, check environmental measurements coming from each sensor in a granular manner. The end result is a first step towards building a smart campus Digital Twin, that can support all the people who are part of the campus life in their daily activities, improving their efficiency and satisfaction, giving them the ability to make informed decisions, and promoting awareness and sustainability.

Copy-paste data augmentation for domain transfer on traffic signs

City streets carry a lot of information that can be exploited to improve the quality of the services the citizens receive. For example, autonomous vehicles need to act accordingly to all the element that are nearby the vehicle itself, like pedestrians, traffic signs and other vehicles. It is also possible to use such information for smart city applications, for example to predict and analyze the traffic or pedestrian flows. Among all the objects that it is possible to find in a street, traffic signs are very important because of the information they carry. This information can in fact be exploited both for autonomous driving and for smart city applications. Deep learning and, more generally, machine learning models however need huge quantities to learn. Even though modern models are very good at gener- alizing, the more samples the model has, the better it can generalize between different samples. Creating these datasets organically, namely with real pictures, is a very tedious task because of the wide variety of signs available in the whole world and especially because of all the possible light, orientation conditions and con- ditions in general in which they can appear. In addition to that, it may not be easy to collect enough samples for all the possible traffic signs available, cause some of them may be very rare to find. Instead of collecting pictures manually, it is possible to exploit data aug- mentation techniques to create synthetic datasets containing the signs that are needed. Creating this data synthetically allows to control the distribution and the conditions of the signs in the datasets, improving the quality and quantity of training data that is going to be used. This thesis work is about using copy-paste data augmentation to create synthetic data for the traffic sign recognition task.