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Q: EM virtual prototyping

Thesis Code: 20014 Thesis Type : Master Thesis for Telecommunication/Electronic Engineering, Computer Science, Mathematics, Physics or equivalent Research Area : Advanced Computing and Applications Requirements MS students in Telecommunication Engineering, Electronic Engineering, Computer Science, Mathematics or equivalent Experience with main programming languages (Matlab /Fortran/C/C++) Basic knowledge of EM fields Good knowledge of linear algebra and linear systems Description Solutions to Maxwell’s equations are known only for a few simple geometries; this is where the scientific discipline known as Computational Electromagnetics (CEM) comes into play, aiming at a numerical solution of the equation in presence of non-trivial geometries/materials. The thesis aims at developing fast and efficient algorithms for the solution of Maxwell’s equations, with special attention to: large patch antenna arrays large and complex structures (e.g., satellites, aircrafts, etc.) Contact : send a resume with attached the list of exams to marco.righero@linksfoundation.com specifying the thesis code and title.

Q: A framework for autonomous driving synthetic data collection

Thesis Code: 19023 Thesis Type : B.Sc. thesis in Computer Science, Mathematics, or equivalent Research Area : Data Science for Industrial and Societal Application Requirements : Knowledge of Python Software development skills Description : Self-driving technology presents a rare opportunity to improve the quality of life in many of our communities. Avoidable collisions, single-occupant commuters, and vehicle emissions are choking cities, while infrastructure strains under rapid urban growth. Autonomous vehicles are expected to redefine transportation and unlock a myriad of societal, environmental, and economic benefits. From a technical standpoint, however, the bar to unlock technical research and development on higher-level autonomy functions like perception, prediction, and planning is extremely high. For this reason, we want to integrate available open data, with synthetic data created with the aid of a virtual environment. The objective of this thesis consists in the study and implementation of a framework useful for collecting data of surrounding vehicles in a virtual environment. This is created with the help of dedicated tools for extracting information from the Grand Theft Auto V graphic engine and AirSim open source simulator. The candidate will have both the task of creating the framework for extracting data and studying and evaluating the best way to save them. The data created will be used for machine learning algorithm in future works. Contact : send a resume with attached the list of exams to mirko.zaffaroni@linksfoundation.com specifying the thesis code and title.

Q: Comparison of different machine learning techniques for estimating the destination of public transport passengers

Thesis Code: 19019 Thesis Type : M.Sc. thesis in Computer Science Research Area : Urban Mobility & Logistic Systems Requirements : Interest in machine learning algorithms Experience with Python Ability to critically interpret analytical results Description: Although smart tickets are increasingly common in public transport, few cities are equipped with an automatic fare collection (AFC) system that can provide information on both boarding (check-in) and alighting (check-out) users’ locations. In fact, usually only check-in ticket validation is mandatory, and it is therefore necessary to estimate the destination of passengers. The student will have to study a deep learning model to estimate the destinations of public transport passengers, using the electronic ticketing data available in the province of Cuneo. The student will be asked to implement the algorithm in Python and validate the model using the data available on the passengers’ check-out. Furthermore, the student will have to evaluate the model’s performance against a Trip-Chaining model currently in use on the same data, identifying the advantages and disadvantages of the different estimation models. The thesis will be carried out in collaboration between the Links Foundation (Urban Mobility & Logistic Systems Area) and the Polytechnic of Turin (Department of Control and Computer Engineering). Contact: send a resume with attached the list of exams to maurizio.arnone@linksfoundation.com specifying the thesis code and title.

Q: A new paradigm of local public transport: flexible and demand-based services

Thesis Code: 19015 Thesis Type : M.Sc. thesis in Environment, Land and Infrastructure Engineering, Data Science, Mathematical Engineering, Mathematics, or equivalent Research Area : Urban Mobility & Logistic Systems Requirements Experience with main programming languages (Python, C/C++) Interest in algorithms Ability to critically interpret analytical results Description The extra-urban public transport services currently in operation are often the result of a planning designed to meet the needs of an outdated mobility demand that is very different from today’s one, since it was characterized by a strong prevalence of systematic travels and a well-defined profile outlined during the day. Today the demand has become much more complex and articulated than once -in terms of travel reasons, schedules and routes- and requires a continuous service during the day, capable of providing “the guarantee of return”. The current transport service does not adequately respond to these new needs. The student will have to study the electronic ticketing data (BIP) of the extra-urban services of the Cuneo area, understand how the users of public transport move in the territory, and study and develop a Python algorithm able to simulate the conversion of the entire extra-urban public transport service in demand-based service (i.e. on-call service), able to customize the offer according to the real needs of the user. The thesis will also address the estimate and the assessment of the economic impacts of such services on companies and users. Contact: send a resume with attached the list of exams to maurizio.arnone@linksfoundation.com specifying the thesis code and title.