Thesis proposals.
LINKS Foundation makes a certain number of thesis proposals available, to be performed at the Foundation’s laboratories under close supervision by LINKS researchers.
If you are a university student at the end of your education path and are interested in discovering what the Foundation can offer to help you towards a professional career in research check the list of currently available thesis proposals right under and send your application to the e-mail address in the post.
You will be contacted by our administration staff to plan an exploratory meeting.
- - Available Theses
- Advanced Computing, Photonics & Electromagnetics
- AI, Data & Space
- Connected Systems & Cybersecurity
- Future cities & communities
Thesis Code: 23003
Thesis Type: M.Sc. thesis in Computer Science, ICT for Smart Societies
Research Domain: Future Cities & Communities
Requirements
• Experience with Python
• Interest in Natural Language Processing
• Skills in descriptive and inferential statistics and in the use of statistical softwares
• Skills on data science (analysis and processing)
• Ability to represent data
Description
For a technology to be widely accepted by the public, it is not enough that it brings benefits. The public needs also to be highly involved in order to understand whether to use new technology. According to several research studies, at present, the level of awareness and understanding of UAM (Unmanned Air Mobility) / UAV (Unmanned Air Vehicles) is very low among people.
The objective of this thesis is to assess the public acceptance of UAV/UAM using statistical models (e.g. UTAUT, TRA, TPB) or by mining social data with text mining / machine learning algorithms related to natural language processing.
Firstly, the candidate will carry out a review of public acceptance evaluation methods with respect to UAV/UAM, defining: domains of application of UAV/UAM, main constraints recognised by the general public, and effective strategies to foster the public acceptance. Then, the candidate will assess public acceptance. The candidate will have both the task of collecting and evaluating the data of the case study. Such a data-driven approach could be helpful to measure community awareness and engagement around UAM/UAV-related topics. Findings can constitute policy insights for those cities and regions that are already equipping themselves to introduce air mobility services.
The thesis will be carried out in collaboration between LINKS and the University of Turin.
Contact: send a resume with attached the list of exams to maurizio.arnone@linksfoundation.com specifying the thesis code and title.
Thesis Code: 23002
Thesis Type: M.Sc. thesis in Environment, Land and Infrastructure Engineering/M.Sc. thesis in Territorial, Urban, Environmental and Landscape Planning/equivalent
Research Area: Future Cities & Communities
Requirements
• Interst in in transport planning and modeling concepts and techniques.
• Familiarity with urban planning and design concepts, such as land use and zoning, urban form and accessibility, and sustainable transportation.
• Experience with data visualization tools, such as PoweBi, Tableau, QGIS, or ArcGIS.
• Understanding of data analysis, statistical methods and ability to critically interpret analytical results.
One or more of the following is considered a plus:
• Familiarity with transportation-related software and tools.
• Experience with Geographic Information Systems (GIS) and spatial analysis.
• Programming skills, such as proficiency in Python, R, or Matlab for data analysis and modeling.
• Experience with machine learning techniques, such as supervised and unsupervised learning, which could be used in the analysis and modeling of autonomous vehicle data.
• Familiarity with relevant regulations and standards related to autonomous vehicles.
Description
The diffusion of autonomous driving vehicles and their impact on urban systems is a rapidly evolving and highly relevant topic of research, with significant implications for the academic, industrial and institutional sectors. As such, this master thesis project offers a unique opportunity for students to delve into the intricacies of this rapidly advancing field.
The project aims to investigate several key issues related to the integration of autonomous vehicles into urban environments, including: The effects of autonomous vehicles on traffic flow and congestion in urban areas. The impact of autonomous vehicles on land use and urban design. The implications of autonomous vehicles on energy consumption and environmental sustainability. The technical and regulatory challenges of integrating autonomous vehicles into existing urban transportation systems. The potential of autonomous vehicles to improve safety and accessibility for disadvantaged groups. The implications of autonomous vehicles on public transportation systems and services. The role of public policy in shaping the diffusion and deployment of autonomous vehicles in urban areas. The interaction between autonomous vehicles and other emerging technologies such as 5G and IoT.
The project will involve the development of different scenarios of autonomous vehicle diffusion, the impact evaluation of these scenarios, and the simulation of urban traffic after a replacement of the circulating vehicle fleet with such vehicles. The City of Turin will serve as a case study, and the results of the research will be used to inform urban planning processes.
The thesis will be carried out in collaboration between the Links Foundation (Urban Mobility & Logistic Systems Area) and the Polytechnic of Turin (Department of Environment, Land and Infrastructure Engineering/Interuniversity Department of Regional and Urban Studies and Planning).
The project provides the opportunity for candidates to develop a range of skills, including transport modeling, urban planning, and data analysis, and will be tailored to the candidate’s specific interests and expertise.
Contact: send a resume with attached the list of exams to maurizio.arnone@linksfoundation.com specifying the thesis code and title.
Thesis Code: 23001
Thesis Type: M.Sc. thesis in ICT for Smart Societies / M.Sc. thesis in Mobility Engineering / M.Sc. thesis in Civil Engineering / M.Sc. thesis in Mathematical Engineering / equivalent
Research Area: Future Cities & Communities
Requirements
• Interest in transport models and planning
• Experience in data analysis
• Experience with main programming languages (Python, C/C++)
• Interest in using Geographic Information Systems (GIS)
• Ability to critically interpret analytical results
Description
The diffusion of autonomous vehicles and their consequent impact on traffic and on the urban system represents an emerging topic of research and of increasing interest in the academic, industrial and institutional fields.
This thesis is conducted as part of a mobility project, the aim of which is to investigate some issues related to the diffusion of autonomous vehicles in the urban environment, including: (i) the development of different scenarios of autonomous vehicles diffusion, that use data on the interactions between autonomous vehicles and other road users; (ii) the development of a traffic simulation environment to evaluate the impacts of different scenarios of autonomous vehicles diffusion on traffic congestion; (iii) the impact of the integration of autonomous vehicles into the urban system, in terms of transport safety and efficiency and environmental impact.
The models can be tested on a case study of interest (e.g. the city of Turin) to outline possible scenarios for the public decision maker and to support urban planning processes. Based on the candidate’s skills and interests, it will be possible to carry out an in-depth analysis on transport models and / or urban planning policies.
Contact: send a resume with attached the list of exams to maurizio.arnone@linksfoundation.com specifying the thesis code and title.
Thesis Code: 22011
Thesis Type: Master Thesis for Computer Engineering, ICT for Smart Societies, Telecommunication Engineering or related fields
Research Area: Connected Systems and Cybersecurity
Requirements
- Excellent software programming skills
- Good knowledge of Mobile applications development
- Experience with Python
- Knowledge of the Linux Operating System
Motivation
The Cooperative, Connected and Automated Mobility (CCAM) encloses several services to enhance the driving and travel experience. The infotainment systems of vehicles can be used to spread these CCAM services. Indeed, the most recent vehicles offer infotainment systems with already some specific environments, such as Android Auto and Apple CarPlay, that can further facilitate the provision of CCAM-devoted services. The exploitation of these assets can foster the creation of CCAM mobile apps that enhance the feature of current infotainment systems.
Objective
The aim of the thesis is to develop a mobile application for the vehicle infotainment system that targets the implementation of the most significant CCAM services, such as road event alerts or smart route planning.
The first part of the thesis will be devoted to analysing the different infotainment systems and related automotive environments. This analysis will be the basis of the design of the mobile application’s framework to be developed. The second part of the thesis will indeed focus on the development of such a framework together with some basic CCAM services. The mobile application framework will be tested within the framework of a European research project.
The student will have the possibility to work in an informal cutting-edge research laboratory using the latest available technologies in the CCAM field.
Contact: send a resume with attached the list of exams and related scores to daniele.brevi@linksfoundation.com specifying the thesis code and title.
Thesis Code: 22010
Thesis Type: Master Thesis for Computer Engineering, ICT for Smart Societies, Telecommunication Engineering or related fields
Research Area: Connected Systems and Cybersecurity
Requirements
- Excellent software programming skills
- Strong experience with Python/bash scripting and Linux environment
- Strong experience with C/C++ programming languages
- Basic knowledge of machine learning
Motivation
Knowledge of the surrounding environment is crucial for connected and autonomous vehicles. These vehicles must rely on their own sensors to identify and keep track of other road users. Significant support can be provided from the roadside infrastructure. In critical places, fixed sensors can continuously sense the surrounding environment to identify vehicles, pedestrians, and obstacles and the infrastructure can communicate the gathered information to the connected vehicles. Visual cameras are typically used, but, nowadays, LiDARs are beginning to be employed as their cost is lowering. New detection and tracking approaches, that exploit LiDAR data, can be now introduced.
Objective
The aim of the thesis is to implement a framework for the automotive environment that provides object detection and tracking based on LiDAR data.
The first part of the thesis will be devoted to surveying the state-of-art solutions of object detection and tracking methods that exploit LiDAR data. The most promising approaches will be implemented in the second part of the thesis. The student will develop an object and detection tracking system exploiting data coming from real sensors installed on the field.
The student will have the possibility to work in an informal cutting-edge research laboratory using the latest available technologies of road sensors and exploiting a real-life testbed.
Contact: send a resume with attached the list of exams and related scores to daniele.brevi@linksfoundation.com specifying the thesis code and title.
Thesis Code: 22009
Thesis Type: Master Thesis for Computer Engineering, ICT for Smart Societies, Telecommunication Engineering or related fields
Research Area: Connected Systems and Cybersecurity
Requirements
- Excellent software programming skills
- Strong experience with Python/bash scripting and Linux environment
- Experience with C/C++ programming languages
- Experience with embedded systems
Motivation
The Cooperative, Connected and Automated Mobility (CCAM) is based on communication among vehicles, infrastructure and other road users. Several applications, that exploit information from connected vehicles, can be developed for improving road safety and for enhancing the travel experience. These solutions are more effective as the number of connected vehicles increases. Nowadays, the number of natively connected vehicles is relatively small compared with the number of circulating vehicles. Enabling the connectivity of legacy vehicles will drastically increase the number of connected vehicles creating a more effective CCAM ecosystem.
Objective
The aim of the thesis is to implement a smart dongle-based solution for enabling connectivity in legacy vehicles. This system is used to share information and position from the vehicles to the infrastructure using 4G/5G mobile network connections.
The first part of the thesis will be devoted to surveying the market for identifying the best hardware solutions to be used on the vehicles (e.g., USB dongle, OBD-II dongle). The survey needs to consider hardware capabilities and communication interface availability. The second part of the thesis will focus on the real implementation of the system. Basic connectivity services from vehicles to the roadside infrastructure will be developed in the thesis’s work and the whole system will be tested within the framework of a European research project.
The student will have the possibility to work in an informal cutting-edge research laboratory using the latest available technologies in the CCAM field.
Contact: send a resume with attached the list of exams and related scores to daniele.brevi@linksfoundation.com specifying the thesis code and title.
Thesis Code: 22007
Thesis Type: Master Thesis for Computer Engineering, ICT for Smart Societies, Telecommunication Engineering or related fields
Research Area: Connected Systems and Cybersecurity
Requirements
• Excellent software programming skills
• Strong experience with Python/bash scripting and Linux environment
• Strong Experience with C/C++ programming languages
• Knowledge of network simulators
Motivation
Cooperative, Connected and Automated Mobility (CCAM) is one of the most complex and interesting topics in the ICT landscape. One of the main challenges for the CCAM is testing newly developed applications and services. The availability of autonomous and connected vehicles together with a safe physical testing area prevents performing effectively pre-testing of new services. The availability of a virtual simulation framework can ease the development and validation process of new CCAM applications.
Objective
The aim of the thesis is to develop a virtualized vehicle simulation framework to be used for testing CCAM applications. The framework will include a virtual On-Board Unit that provides the vehicle-side features. This module will be connected to cloud infrastructure services through a simulated 5G mobile network.
The first part of the thesis will be devoted to identifying the best approaches for building the virtual module implementing the vehicle’s On-Board Unit. Furthermore, the student will analyse the panorama of open-source projects that provide a 5G mobile network simulation framework. The second part of the thesis will focus on the real implementation of the virtualized vehicle simulation framework including the integration of the 5G mobile network simulator. The overall framework will be tested within the framework of a European research project.
The student will have the possibility to work in an informal cutting-edge research laboratory using the latest available technologies in the CCAM field.
Contact: send a resume with attached the list of exams and related scores to daniele.brevi@linksfoundation.com specifying the thesis code and title.
Thesis Code: 22008
Thesis Type: Master Thesis for Computer Engineering, ICT for Smart Societies, Telecommunication Engineering or related fields
Research Area: Connected Systems and Cybersecurity
Requirements
- Excellent software programming skills
- Strong experience with Python/bash scripting and Linux environment
- Good knowledge of C/C++ programming languages
Motivation
A precise knowledge of other vehicles, road users and obstacles is crucial for autonomous vehicles to perform safe road manoeuvres. The connected and autonomous vehicles can receive support from the roadside infrastructure regarding the position of obstacles and of other road actors. The roadside infrastructure can exploit data from camera and/or LiDAR for identifying the objects. One key step in the information-gathering process is the computation of the object’s position. This step is particularly complex if only data from the camera are available. In this case, object georeferencing techniques must be applied.
Objective
The aim of the thesis is to implement an object georeferencing system to be used for computing the objects’ position in a road environment.
The first part of the thesis will be devoted to surveying the different georeferencing techniques that have been introduced in the literature. The target of the survey is to select the most promising techniques that can be used with data from a camera and/or from a LiDAR. The second part of the thesis will focus on a real implementation of the selected georeferencing techniques that will be tested and evaluated using data from a real system made of a visual camera and a solid-state LiDAR.
The student will have the possibility to work in an informal cutting-edge research laboratory using the latest available technologies of road sensors and exploiting a real-life testbed.
Contact: send a resume with attached the list of exams and related scores to daniele.brevi@linksfoundation.com specifying the thesis code and title.
Thesis Code: 22005
Thesis Type: M.Sc. thesis in Computer Engineering, ICT for Smart Societies, e-Health Engineering or equivalent
Research Area: Future Cities & Communities (FCC)
Requirements:
- Knowledge of Java Spring
- Software development skills
- Interest in web-app developing
- Basic knowledge of REST architecture
Description
Telemedicine is the practice of medicine using technology to deliver care remotely. Telemedicine services work as support with the traditional healthcare services, providing a broader approach to deliver medical treatments.
The practice of telemedicine, where applicable, benefits both the patient and the healthcare system in multiple ways:
- Lower healthcare costs
- No time and financial expenses for the patient to reach the hospital.
- Provide your patients better access to healthcare services
- Avoid the risks of exposure to contagious patients, by not coming to the hospital.
- Reduce number of patients inside of hospitals.
For its characteristics, telemedicine is very suitable for follow-up visits, for the management of chronic illnesses and medication management. In this category of patients, we find those who suffer from chronic cardiovascular diseases. These patients need to monitor their health status continuously for a lifetime.
The trend of adoption of telemedicine practices among hospitals has seen a major increase during the Covid-19 pandemic.
The thesis will be structured as follows:
- State-of-the-art analysis of EMR already used;
- Design and development of a first prototype;
- Testing of the prototype;
- Drawing conclusions and formulating a research roadmap.
Contact: send a resume with attached the list of exams to maurizio.arnone@linksfoundation.com specifying the thesis code and title.
Thesis Code: 22002
Thesis Title: Automatic design of Metasurface Antennas
Thesis Type: Master Thesis for Telecommunication/Electronic Engineering, Computer Science, Mathematics, Physics or equivalent
Research Area: Advanced Computing, Photonics and Electromagnetics (CPE)
Requirements
- MS students in Telecommunication Engineering and Electronic Engineering
- Basic knowledge of EM fields
- Experience with Matlab
- Knowledge of antenna modelling software (CST, Feko, etc.)
Description
Metamaterials are artificial materials composed of various inclusion types embedded in a host medium in specific arrangements. Unusual metamaterial electromagnetic behavior can be achieved with metamaterials (e.g. cloaking), by leveraging both the properties of the elementary constituent materials and the inclusion spatial arrangement. A metasurface (MTS) [1] is a thin metamaterial layer (2D metamaterial). MTS can be designed to provide engineered boundary conditions for controlling the propagation of surface waves and radiation [2]. Applications of MTS range from on board antennas for satellite communications (low profile, high performances antennas) to biomedical and nanoscale application.
This thesis aims at the automatic design of MTS antennas using in-house modelling and optimization codes, at the validation of the designed MTS antennas with commercial software, and at the improvement of the modelling and optimization codes to face new and challenging scenarios.
References
- Faenzi, et al. “Metasurface Antennas: New Models, Applications and Realizations”, Sci. Rep. Vol. 9, 2019
- A. Francavilla, E. Martini, S. Maci and G. Vecchi, “On the Numerical Simulation of Metasurfaces With Impedance Boundary Condition Integral Equations”, IEEE Transactions on Antennas and Propagation, vol. 63, no. 5, 2015.
Contact: send a resume with attached the list of exams to marco.righero@linksfoundation.com specifying the thesis code and title.
Thesis Code: 22004
Thesis Type: Master Thesis for Telecommunication/Electronic Engineering
Research Area: Advanced Computing, Photonics and Electromagnetics (CPE)
Requirements
- MS students in Telecommunication Engineering, Electronic Engineering, or equivalent
- Basic knowledge of EM fields
- Experience with Matlab
- Experience with FPGA programming and simulation (Verilog, VHDL)
- Good knowledge of basic DSP algorithms
Description
Fat intra-body communication (Fat-IBC) is an innovative technique exploiting the very low electrical conductivity of the fat tissue layer (0.11 S/m) to transmit electromagnetic signals through the human body [1]. This technique is really promising for the implementation of wireless, in-body, bidirectional Brain-Machine-Body connectivity, providing an excellent low-loss communication channel for implantable and wearable networks, such as inter-connect wireless medical sensors [2]. This thesis falls within the EU H2020 FET Open project B-CRATOS (“Wireless Brain-Connect inteRfAce TO machineS”, https://www.b-cratos.eu/) (965044). One of the objectives of this project is to implement a bidirectional wireless connection system between brain and a prosthetic arm, paving the way to the creation of a proof-of-concept, revolutionary untethered brain-machine interface. To verify the feasibility of this system, non-human primates (NHP) will be considered for non-invasive testing.
This thesis aims at the design of wearable aggregators comprising of properly optimized epidermal antennas and modulation-demodulation electronics to communicate simultaneously to/from neural transceivers. Tests on numerical and realistic phantoms are foreseen within the thesis period.
References
- B. Asan et al., “Intra-body microwave communication through adipose tissue,” Healthc. Technol. Lett., vol. 4, no. 4, pp. 115-21, 2017.
- B. Asan, et al., “Data packet transmission through fat tissue for wireless intrabody networks,” IEEE J. Electromagn., RF, Microw. Med. Biol., vol. 1, no. 2, pp. 43-51, 2017.
Contact: send a resume with attached the list of exams to rossella.gaffoglio@linksfoundation.com specifying the thesis code and title.
Thesis Code: 22003
Thesis Type: Master Thesis for Telecommunication/Electronic Engineering
Research Area: Advanced Computing, Photonics and Electromagnetics (CPE)
Requirements
• MS students in Telecommunication Engineering, Electronic Engineering, or equivalent
• Experience with embedded software programming using the C language
• Knowledge of data acquisition and control systems
• Basic knowledge of PCB design
• Good knowledge of basic concepts of RF electronics
• Basic knowledge of EM fields
• Experience with electronic instruments (the thesis will include laboratory activities)
Description
Microwave cancer hyperthermia is a type of medical treatment in which tumor cells are selectively exposed to a supra-physiological temperature (42/43 °C) using proper antenna systems [1]. For internal tumors, this is currently achieved by means of an array of antennas equipped with a proper cooling system (the water bolus) to avoid overheating of the skin [2]. Since the effectiveness of a hyperthermia treatment is strictly dependent on the quality of the heating process, a treatment planning is fundamental to optimally set the amplitudes and phases of the applied signals. In order to maximize the effectiveness of the selective heating process, a real-time control of the antenna feeding parameters is required, together with an active control system to correct any non-ideal behaviour, thus ensuring the gain and phases remain constant throughout the whole operation of the system.
Starting from a demonstrator reproducing an array applicator for hyperthermia in the head and neck region, this thesis aims at designing an all-in-one solution for the control of the antenna array, including the source signal generation, the measurement, and the control system, on a single PCB.
References
1. N. R. Datta et al., “Local hyperthermia combined with radiotherapy and-/or chemotherapy: Recent advances and promises for the future,”, Cancer Treat. Rev., vol. 41, no. 9, pp. 742-53, 2015.
2. M. M. Paulides et al., “The HYPERcollar: A novel applicator for hyperthermia in the head and neck,” Int. J. Hyperthermia, vol. 23, no. 7, pp. 567-76, 2007.
Contact: send a resume with attached the list of exams to rossella.gaffoglio@linksfoundation.com specifying the thesis code and title.
Thesis Code: 21002
Thesis Type: M.Sc. thesis in Computer Science
Research Area: Urban Mobility & Logistic Systems
Description
For a technology to be widely accepted by the public, it is not enough that it brings benefits. The public needs also to be highly involved in order to understand whether using a new technology. According to several research studies, at present the level of awareness and understanding of UAM (Unmanned Air Mobility) / UAV (Unmanned Air Vehicles) is very low among people.
The objective of this thesis is to assess the public acceptance of UAV/UAM by mining social data with text mining / machine learning algorithms related to natural language processing.
Firstly, the candidate will carry out a review of public acceptance evaluation methods with respect to UAV/UAM, defining: domains of application of UAV/UAM, main constraints recognised by the general public, and effective strategies to foster the public acceptance.
Then, the candidate will assess public acceptance of UAV/UAM by mining social data with proper text mining techniques. The candidate will have both the task of collecting the data and evaluating the best algorithms to apply for the case of study. Such data-driven approach could be helpful to measure the community engagement around the UAM/UAV-related topics. Findings can constitute insights from which public policy makers may draw for enhancing the community involvement around these topics and for becoming far more reactive to the citizenry’s needs.
The thesis will be carried out in collaboration between Links and the University of Turin.
Requirements
- Experience with Python
- Basic concepts on data science, concerning data analysis and data processing
- Ability to represent data
- Interest in Natural Language Processing
Contact: send a resume with attached the list of exams to maurizio.arnone@linksfoundation.com specifying the thesis code and title.
Thesis Code: 21001
Thesis Type: Global Navigation Satellite System (GNSS), Computer Science, Cybersecurity
Research Area: Cybersecurity of systems based on GNSS data
Requirements:
- Knowledge of GNSS, GNSS data and GNSS receivers
- Knowledge of Linux Operating System
- Interest in Cybersecurity and Trust technologies
- Curiosity-driven mindset
Description
GNSS technologies has been constantly growing in the last years and GNSS receivers have been adopted in the most different fields of applications such as: road tolling, secure autonomous driving, location-based services, synchronization of networks (e.g. telco, energy grids, etc.), financial transactions. GNSS receivers and connected devices integrating and making use of these receivers are all vulnerable to intentional attacks exploiting different attack vectors (e.g. GNSS signals, operating systems & software and communication networks). The feasible chance to exploit vulnerabilities and intentionally modify GNSS data create incentives for the attackers that want to impair or fool any systems that has a dependency in GNSS. Every system that make use of GNSS data, either when they are estimated from the satellite constellation or received from a network peer must solve/answer the same question: can I trust the GNSS data and take safe decisions and operate in accordance to them? There is, therefore, a pressing need to analyze threats and vulnerabilities along the whole chain (i.e. from satellite to system and user on earth) to designs, develop and test solution to digitally Trust in GNSS data.
The thesis will be structured as follows:
- analysis of threats and vulnerabilities of a reference system that makes use of GNSS data;
- state-of-the-art analysis of cyber technologies to “Trust-by-verify” GNSS data;
- design and development of a simple proof-of-concept (PoC);
- in-lab testing;
- drawing conclusions and formulating a research roadmap.
Contact: send a resume with attached the list of exams to andrea.vesco@linksfoundation.com specifying the thesis code and title.
Thesis Code: 20019
Thesis Type: Master Thesis for Telecommunication/Electronic Engineering, Biomedical Engineering, Computer Science, Mathematics, Physics or equivalent
Research Area: Advanced Computing, Photonics and Electromagnetics (CPE)
Requirements
- MS students in Telecommunication Engineering, Electronic Engineering, Biomedical Engineering, Computer Science, Mathematics, Physics or equivalent
- Basic knowledge of EM fields
- Experience with Matlab
- Good knowledge of linear algebra and linear systems
Description
Hyperthermia is a type of cancer treatment in which tumors are exposed to a supra-physiological temperature (42/43 °C) by means of proper antenna systems to sensitize cancer cells towards radiation and drugs [1]. Temperature control is crucial in hyperthermia treatments, to check the effectiveness of the heating in the target region and to avoid dangerous hotspots in the surrounding healthy tissues. In current clinical practice, temperature monitoring is achieved in an invasive manner, with temperature probes inserted into closed-tip catheters [2]. An extensive and innovative use of high-performance simulations carried out prior to treatment seems to be a promising way to produce accurate and reliable temperature maps during treatment from a minimal number of direct measurement points. This could provide dual benefit to the patient, yielding accurate temperature estimations in points where temperature is not known, and reducing the infection risk via a minimal use of catheters.
This thesis aims at implementing an efficient “library” of high-performance simulations of a numerical phantom, verifying the possibility to obtain reliable temperature maps of the whole region of interest from scarce data acquisition. A high-fidelity computable human phantom from the Virtual Population of the simulation software Sim4Life will be used for this study.
References
- R. Datta et al., “Local hyperthermia combined with radiotherapy and-/or chemotherapy: Recent advances and promises for the future,”, Cancer Treat. Rev., vol. 41, no. 9, pp. 742-53, 2015.
- M. Paulides et al., “Status quo and directions in deep head and neck hyperthermia,” Radiat. Oncol., vol. 11, no. 21, pp. 809-21, 2016.
Contact: send a resume with attached the list of exams to rossella.gaffoglio@linksfoundation.com and marco.righero@linksfoundation.com specifying the thesis code and title.
Thesis Code: 20017
Thesis Type: Master Thesis for Telecommunication/Electronic Engineering, Computer Science, Mathematics, Physics or equivalent
Research Area: Advanced Computing, Photonics and Electromagnetics (CPE)
Requirements
- MS students in Telecommunication Engineering, Electronic Engineering, Computer Science, Mathematics, Physics or equivalent
- Experience with main programming languages (Python/Matlab /Fortran/C/C++)
- Basic knowledge of EM fields and wave propagation
- Basic knowledge of numerical methods for ODEs integration
Description
The link communication from/to satellite, re-entry or space vehicles is often subject to degradation known as black-out. To assess this issue, radio frequency (RF) wave propagation through complex media [1] (such ionosphere, plasmas and complex gas mixtures) must be considered. Asymptotic techniques such as ray or beam tracing [2] can be used to predict EM propagation in these inhomogeneous media. Coupled with integral equations for free-space radiation they provide a powerful numerical tool to design antennas for critical applications.
This thesis aims to develop and improve both physical model and numerics of the exiting tools in order to improve accuracy and range of applications for RF complex media propagation. The improved model will be applied in the analysis of communication link of real re-entry vehicle in earth or extra-terrestrial atmosphere.
References
- A. Kravtsov, Y.I. Orlov, “Geometrical Optics of Inhomogeneous Media”, In: Springer Serie on Wave Phenomena, vol 6, Springer, Berlin 1990.
- Kim and L. Ling, “Electromagnetic Scattering by Inhomogeneous Object by Ray Tracing” IEEE Trans. Antennas Propagat., Vol. 40 No.5 May 1992.
Contact: send a resume with attached the list of exams to andrea.scarabosio@linksfoundation.com specifying the thesis code and title.
Thesis Code: 20016
Thesis Type: Master Thesis for Telecommunication/Electronic Engineering, Computer Science, Mathematics, Physics or equivalent
Research Area: Advanced Computing and Applications
Requirements
- MS students in Computer Science
- Experience with main programming languages (Matlab /Fortran/C/C++)
- Knowledge of parallel computing (MPI)
Description
Numerical simulations are routinely employed to model complex systems, as electromagnetic waves propagation. In a similar way, maps are used to describe physical locations [1]. However, if one wants to study different responses varying the parameters of the systems, the computational burden becomes too large, and often just a reduced part of the system output is of interest. In the maps analogy, one is interested just in the public transportation network to plan different routes. From a computational point of view, simpler and compact models can be constructed from representative solutions of the complete system, using different techniques to combine them together [1,2].
This thesis aims at porting existing code for electromagnetic simulation on a distributed architecture, with the specific aim of using it for building reduced order models for Computational Electromagnetics.
References
- C. Antoulas Approximation of Large-Scale DynamicalSystems, 2005, SIAM
- Hochman, J.Fernandez Villena, A. G. Polimeridis, L. M. Silveira, J. K. White, L. Daniel, ‘Reduced-Order Models for Electromagnetic Scattering Problems’, Antennas and Propagation, IEEE Transactions on , vol.62, no.6, pp.3150, 3162, April 2014, doi: 10.1109/TAP.2014.2314734
- London images are from Wikipedia, GoogleMaps, and Transportation for London
Contact: send a resume with attached the list of exams to marco.righero@linksfoundation.com specifying the thesis code and title.
Thesis Code: 20015
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 or equivalent
- Experience with main programming languages (Matlab /Fortran/C/C++)
- Basic knowledge of EM fields
- Basic knowledge of linear algebra and linear systems
- Experience with Electronic instruments (the thesis will include laboratory activities)
Description
The exhaustive RF end-to-end testing of an antenna can be complex and time consuming. Due to the sampling criteria limit, the measurement time associated with these complex tests becomes easily prohibitive. Advanced strategies for end-to-end test time reduction are very appealing and recently [1,2], an algorithm based on a properly hybridization of measurements and simulations has been proposed, to demonstrate the possibility to perform a radical under sampled field measurement of the Antenna Under Test (AUT), with respect to the conventional Nyquist criteria.
The thesis would like to improve the performance of the algorithm by investigating the possibility to extend the method to other domains (e.g. frequency, space, etc.).
References
- J. Foged, L. Scialacqua, M. Bandinelli, M. Bercigli, F. Vipiana, G. Giordanengo, M. Sabbadini, and G. Vecchi, “Numerical Model Augmentend RF Test Techniques,” in 6th European Conference on Antennas and Propagation, EuCAP, March 2012.
- J. Foged, L. Scialacqua, F. Saccardi, M. Bandinelli, M. Bercigli, G. Guida, F. Vipiana, G. Giordanengo, M. Sabbadini, and G. Vecchi, “Innovative Approach for Satellite Antenna Integration and Test/Verification,” in 34th Symposium of the Antenna Measurement Techniques Association (AMTA), October 2012.
Contact: send a resume with attached the list of exams to giorgio.giordanengo@linksfoundation.com specifying the thesis code and title.
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.
Thesis Code: 20013
Thesis Type: Thesis in Mechatronic/Telecommunication/Electronic Engineering, Computer Science or equivalent
Research Area: IOT & Pervasive Technologies
Requirements:
• Good knowledge of MATLAB
• Basic skill in software development (C/C++ and Python)
• Good knowledge in mathematical derivation
• Proactive mindset, problem-solving oriented
• Data processing skills
• Familiarity with embedded platforms (Raspberry PI) and Linux environment
Description:
• Motivation
Recently, autonomous navigation of UAVs for indoor environments has received increasing attention from the research community. In fact, UAVs are going to be adopted also in these environments to support various applications, for instance, for logistic operations, to perform some inspection and monitoring tasks in industrial plants as well as in greenhouses. In these scenarios, it is fundamental to know not only the UAV’s position but also its attitude.
Regarding the position estimation, usually the Ultra-Wideband (UWB) technology is employed providing accurate Time of Arrival (ToA) measurements while for the attitude estimation, IMU sensors are typically used allowing to estimate roll and pitch of the UAV. However, the estimation of the yaw angle employing a compass sensor is too inaccurate because the Earth’s magnetic field in indoor environments is heavily affected by electric and electronic devices as well as surrounding metallic objects and structures.
To overcome this limitation, the UWB technology is becoming also a promising solution to estimate the angle with which the transmitted UWB signal arrives at the receiver, thus, exploiting this type of measurement, it is possible to estimate the yaw angle of the UAV. In particular, the UWB technology can be used to perform Angle of Arrival (AoA) measurements employing antenna array at the receiver.
• Objectives
The goal of this thesis is to design a hybrid localization algorithm based on UWB enabling autonomous operations of UAVs in indoor environments. The hybrid algorithm will combine both ToA and AoA measurements to estimate both position and attitude of the UAV by using Bayesian methods like Extended Kalman Filter (EKF).
Firstly, the designed algorithm will be tested via computer simulations and iteratively optimized. After that, the optimized algorithm will be implemented in UWB devices and the performance evaluated in real indoor environments.
Contact: send a resume with attached the list of exams passed during the Bachelor of Science and Master of Sciences to francesco.sottile@linksfoundation.com or pert@linksfoundation.com specifying the thesis title.
Thesis Code: 20012
Thesis Type: Computer Science, Cybersecurity
Research Area: Cybersecurity
Requirements:
- Experience with main Programming Languages (C/C++; JavaScript; Python)
- Knowledge of Cybersecurity and Internetworking
- Basic knowledge of Distributed Ledger Technologies (DLT)
- Curiosity-driven mindset
Description
The Internet of Things (IoT) is a quickly growing segment of today’s Internet, enabling connectivity between smart devices. Digital Trust among IoT devices needs to be built and data properly protected. Distributed Ledger Technologies (DLTs) play an important role to secure IoT. Among the different DLTs, the IOTA Ledger [1] is well suited to serve the IoT of the world by providing a simple and efficient way to build digital trust among devices and secure the integrity and verifiability of data exchanged.
The thesis will be structured as it follows:
- analysis of the IOTA Ledger: the Tangle;
- deployment of a private Tangle;
- design and development of an application to securely interact with the Tangle: write & read stream of IoT data;
- in-lab testing verification and measurement of the performance of the approach.
References
[1] IOTA Foundation, IOTA Tangle; available at https://www.iota.org
Contact: send a resume with attached the list of exams to andrea.vesco@linksfoundation.com specifying the thesis code and title.
Thesis Code: 20011
Thesis Type: M.Sc. thesis in Machine Learning, Data Science, Computer Science, Mathematics, or equivalent
Research Area: Data Science for Industrial and Societal Application
Requirements:
- Knowledge of Python
- Software development skills
- Basic concepts on data science, concerning data analysis, processing and machine learning
- Basic concepts on image processing
Description:
Monitoring phenology of agricultural plants is a critical understanding in precision agriculture. Vital improvements can be achieved with precise detection of phenological change of plants which would henceforth improve the timing for the harvest, pest control, yield prediction, farm monitoring, disaster warning etc. Many countries across the world have been developing initiatives to build national agriculture monitoring network systems, since inferring the phenological information contributes to a better understanding of relationships between productivity, vegetation health and environmental conditions.
The objective of this thesis consists in the study and implementation of machine learning algorithms useful for plants phenology monitoring. The proposed algorithms will be trained using open datasets and proprietary datasets. The candidate will have both the task of creating the dataset and studying and evaluating the best algorithms to apply for the case of study.
The candidate is required to implement machine learning algorithms, with the exploratory possibility of deep learning algorithms using popular framework (TensorFlow, PyTorch, Keras, etc..).
Contact: send a resume with attached the list of exams to mirko.zaffaroni@linksfoundation.com specifying the thesis code and title.
Thesis Code: 20010
Thesis Type: M.Sc. thesis in Machine Learning, Data Science, Computer Science, Mathematics, or equivalent
Research Area: Data Science for Industrial and Societal Application
Requirements:
- Knowledge of Python
- Software development skills
- Basic concepts on data science, concerning data analysis, processing and machine learning
- Basic concepts on image processing
Description:
During recent years, the topic of air quality is increasingly raising the attention of institutions and governments, for both the impact on health and the direct influence on climate change. Activists and environmentalist organizations are complaining more and more heavily, bringing the topic to the attention of most of the population. A significant proportion of Europe’s population lives in areas, especially cities, where exceedances of air quality standards occur ozone, nitrogen dioxide and particulate matter (PM) pollution pose serious health risks. On purpose, municipalities can benefit from constant monitoring of the pollutants in sensitive areas, for better handling measures to limit their concentration in the air. Moreover, the chance to exploit meteorological data for an estimate of its trend will improve the potential to take the phenomena under control.
This thesis proposes to build a system for processing of the data acquired from the ESA’s newer mission: Copernicus Sentinel-5P. The fleet of satellites continuously acquires information about several pollutants in the atmosphere, since December 2018. The candidate will build a system for the visualization of the aforementioned data, able to display the most recent acquisitions, given a certain area of interest. Moreover, the system will process the information from different sources, considering different features, such as meteorological data and information acquired from other Copernicus spatial missions (i.e. spectral data, SAR data).
Through the examined sources, the candidate will design and develop at least a machine learning/deep learning model to provide an accurate forecast of the evolution of pollutant concentration. It will benefit from a complete dataset and structures to access and process satellite data.
Contact: send a resume with attached the list of exams to alessandro.farasin@linksfoundation.com specifying the thesis code and title.
Thesis Code: 20009
Thesis Type: M.Sc. thesis in Machine Learning, Data Science, Computer Science, Mathematics, or equivalent
Research Area: Data Science for Industrial and Societal Application
Requirements:
- Knowledge of Python
- Software development skills
- Basic concepts on data science, concerning data analysis, processing and machine learning
- Basic concepts on image processing
Description:
Historical architecture is an important part of Italy’s cultural heritage, so good maintenance of these buildings is crucial for their preservation. Infrared thermography offers a method of visualization that is nondestructive and capable of revealing various types of archaeological anomaly. It can be detected the presence of moisture due to condensation or capillary rise, that can damage the plaster or fresco. Also it can be detected the presence of mold below the surface. The thermal imaging camera can also be used to check the state of adhesion between plaster and the underlying structure or to detect hidden cracks and the presence of infill, or spot previous renovations and hidden structures, but also detect damage caused by an earthquake. With the information gained from thermographic surveys using a thermal imaging camera the preservation of these highlights of Italian culture is ensured.
The objective of this thesis consists in the study and implementation of machine learning algorithms useful for recognizing and report archaeological anomalies highlighted by thermal cameras. The proposed algorithms will be trained using a synthetic dataset and open datasets. The candidate will have both the task of creating the dataset and studying and evaluating the best algorithms to apply for the case of study.
The candidate is required to implement machine learning algorithms, with the exploratory possibility of deep learning algorithms using popular framework (TensorFlow, PyTorch, Keras, etc..).
Contact: send a resume with attached the list of exams to mirko.zaffaroni@linksfoundation.com specifying the thesis code and title.
Thesis code: 20008
Thesis Type: M.Sc. thesis in Machine Learning, Data Science, Computer Science, Mathematics, or equivalent
Research Area: Data Science for Industrial and Societal Application
Requirements:
- Knowledge of Python
- Software development skills
- Basic concepts on data science, concerning data analysis, processing and machine learning
- Basic concepts on Natural Language Processing
Description:
One of the main applications in Natural Language Processing is the categorization of documents based on the topic. In the case of plural categorization, the task is named Multi-label classification, which aims to categorize instances into none, one or more classes. It is one of the first steps for knowledge representation, that is the process of transforming a sequence of unstructured data, into sets of linked and organized concepts.
The objective of this thesis consists in the study and implementation of machine learning and/or deep learning algorithms for extracting and representing the knowledge concealed in sentences and paragraphs. The work will be set to an incremental difficulty, starting from an initial categorization of the available documents through the Multi-label classification task, to the representation of concepts and their interconnections, by means of existing ontologies and services (such as WordNet, ConceptNet, FrameNet). Between the two ends, the candidate will explore the Natural Language processing chain, such as tokenization, pos tagging, lemmatization, stop-words filtering, dependency parsing and named entity recognition. The candidate will have both the task of collecting the data and evaluating the best algorithms to apply for the case of study.
The work has to be performed with NLP algorithms including deep learning algorithms using a popular framework (TensorFlow, PyTorch, Keras, etc..).
Contact: send a resume with attached the list of exams to edoardo.arnaudo@linksfoundation.com specifying the thesis code and title.
Thesis code: 20007
Thesis Type: M.Sc. thesis in Machine Learning, Data Science, Computer Science, Mathematics, or equivalent
Research Area: Data Science for Industrial and Societal Application
Requirements:
- Knowledge of Python
- Software development skills
- Basic concepts on data science, concerning data analysis, processing and machine learning
- Basic concepts on image processing
Description:
Misdiagnosis of the many diseases impacting agricultural crops can lead to misuse of chemicals leading to the emergence of resistant pathogen strains, increased input costs, and more outbreaks with significant economic loss and environmental impacts. Current disease diagnosis based on human scouting is time-consuming and expensive, and although computer-vision based models have the promise to increase efficiency, the great variance in symptoms due to age of infected tissues, genetic variations, and light conditions within trees decreases the accuracy of detection.
The objective of this thesis consists in the study and implementation of machine learning algorithms useful for plants pathology detection. The proposed algorithms will be trained using open datasets. The candidate will have both the task of creating the dataset and studying and evaluating the best algorithms to apply for the case of study.
The candidate is required to implement machine learning algorithms, with the exploratory possibility of deep learning algorithms using popular framework (TensorFlow, PyTorch, Keras, etc..).
Contact: send a resume with attached the list of exams to mirko.zaffaroni@linksfoundation.com specifying the thesis code and title.
Thesis code: 20006
Thesis Type: M.Sc. thesis in Machine Learning, Data Science, Computer Science, Mathematics, or equivalent
Research Area: Data Science for Industrial and Societal Application
Requirements:
- Knowledge of Python
- Software development skills
- Basic concepts on data science, concerning data analysis, processing and machine learning
- Basic concepts on Natural Language Processing
Description:
Nowadays, the amount of information available is, very often, much higher than the time available for manual inspection: this is even more true in contexts implying decisions, which need a wide understanding of the involved domain. Part of this knowledge is concealed in unstructured sources, such as text, that can be processed and transformed in logical structures through Natural Language Processing.
The objective of this thesis consists in the study and implementation of machine learning and/or deep learning algorithms related to natural language processing. The proposed algorithms will be trained using open data, available through APIs. The candidate will have both the task of collecting the data and evaluating the best algorithms to apply for the case of study. The goal is the extraction of concepts related to numerical indicators from the examined documents and infer new knowledge through statistical analysis.
The work has to be performed with NLP algorithms including deep learning algorithms using a popular framework (i.e. TensorFlow, PyTorch, Keras).
Contact: send a resume with attached the list of exams to edoardo.arnaudo@linksfoundation.com specifying the thesis code and title.
Thesis code: 20005
Thesis Type: M.Sc. thesis in Machine Learning, Data Science, Computer Science, Mathematics, or equivalent
Research Area: Data Science for Industrial and Societal Application
Requirements:
- Knowledge of Python
- Software development skills
- Basic concepts on data science, concerning data analysis, processing and machine learning
- Basic concepts on generative models
Description:
Nowadays we have seen generative adversarial networks very successful in creating images, in fact they have proven themselves to be capable of creating hyper-realistic faces, animating paintings, colorizing sketches, etc… However, these models cannot handle only images but also text and audio. Anyway, the latter is a field that remains largely unexplored. For this reason, we want to try to build generative models capable of creating new and unpublished melodies given a set of sounds as input data. The constrain is that sounds created must also have a melody that is harmonious, not just a random sequence of the input sounds. As a bonus we would like to consider the creation of melodies based on a target feeling that we decided to arouse.
The objective of this thesis consists in the study and implementation of machine learning algorithms useful for generating new ad unreleased melodies starting from a set of basic sounds. The proposed algorithms will be trained using a collection of open dataset and private data provided by a company in the sector, with which the candidate will have the opportunity to interact during his thesis work. The candidate will have both the task of creating the training dataset and studying and evaluating the best algorithms to apply for the case of study.
The candidate is required to implement deep adversarial learning algorithms using popular framework (TensorFlow, PyTorch, Keras, etc..).
Contact: send a resume with attached the list of exams to mirko.zaffaroni@linksfoundation.com specifying the thesis code and title.
Thesis code: 20004
Thesis Type: M.Sc. thesis in Machine Learning, Data Science, Computer Science, Mathematics, or equivalent
Research Area: Data Science for Industrial and Societal Application
Requirements:
- Knowledge of Python
- Software development skills
- Basic concepts on data science, concerning data analysis, processing and machine learning
- Basic concepts on image processing
Description:
Monitoring and recognizing workers actions is important, especially in areas where machinery is in operation and in motion, or in scenarios that are becoming more and more current: where workers work together with moving robots. For these reasons it is necessary to have systems capable of monitoring the workers in order to guarantee their safety and health. A thesis is proposed where using video cameras placed in work environments, the employee’s clothing is analysed in order to assess whether he wears all the safety equipment provided, and if not, send a warning signal on a wearable device or smartphone. Also, analyse the actions performed and advise if the worker performs actions that are not appropriate.
The objective of this thesis consists in the study and implementation of machine learning algorithms useful for recognizing and report negative actions performed by workers in a video sequence. The proposed algorithms will be trained using a synthetic dataset and open datasets. The candidate will have both the task of creating the dataset and studying and evaluating the best algorithms to apply for the case of study. The candidate is required to implement machine learning algorithms, with the exploratory possibility of deep learning algorithms using popular framework (TensorFlow, PyTorch, Keras, etc..). tools for extracting information from the Grand Theft Auto V graphic engine. The candidate will have both the task of creating the dataset and studying and evaluating the best algorithms to apply for the case of study.
The type of algorithms being studied and tested are to be classified among those of video analysis, with the exploratory possibility of deep learning algorithms using popular framework (TensorFlow, PyTorch, Keras, etc..).
Contact: send a resume with attached the list of exams to mirko.zaffaroni@linksfoundation.com specifying the thesis code and title.
Thesis Code: 20003
Thesis Type: M.Sc. thesis in Machine Learning, Data Science, Computer Science, Mathematics, or equivalent
Research Area: Data Science for Industrial and Societal Application
Requirements:
- Knowledge of Python
- Software development skills
- Basic concepts on data science, concerning data analysis, processing and machine learning
- Basic concepts on machine learning
Description:
For parcel delivery services it is essential to have a system able to certify the delivery of the package by the courier. Therefore, in a scenario where companies are embarking on the digitalization race, it is necessary for logistics services to have smart media to speed up and make delivery certification methods more precise. For this reason, we propose a thesis that aims to create an intelligent system based on speech recognition and vocal recognition that allows at the same time to recognize the confirmation words pronounced by the recipient and to certify that the person who is receiving the package belongs to a list of suitable users.
The objective of this thesis consists in the study and implementation of machine learning algorithms useful for speech and vocal recognition. The proposed algorithms will be trained using open datasets. The candidate will have both the task of collecting the benchmark datasets and evaluating the best algorithms to apply for the case of study. The work has to be performed with audio processing algorithms including deep learning algorithms using popular framework (TensorFlow, PyTorch, Keras, etc..).
An optional step could be the study and the deploy of the algorithms in wearable devices (smart watches, smart band, etc..).
Contact: send a resume with attached the list of exams to mirko.zaffaroni@linksfoundation.com specifying the thesis code and title.
Thesis Code: 20001
Thesis Type: Thesis in Computer Science, Telecommunication/Electronic Engineering or equivalent
Requirements:
• Good skills in software programming using Java.
• Knowledge of cloud computing (considered a plus).
Description:
Motivation: The next generation of Smart City and Industry 4.0 applications will be geographically distributed, heterogeneous, co-evolving software ecosystems, significantly more sophisticated than the current Enterprise or Cloud compute environments. To be economically sustainable and achieve solution longevity, these software ecosystems must be operationally simple, cost effective to maintain over extended periods of time, and able to cost effectively adapt to both changing environmental conditions and service requirements. To fulfill this, building an IoT application with a modular OSGi approach future–proofs investments that allows to reduce development cycles and enables rapid and cost-effective delivery of new services to these connected devices is highly demanded.
Objectives: The goal of this thesis is to develop an OSGi-based framework supporting the dynamic deployment, monitoring and configuration of distributed microservices implementing cooperative intelligence logics in a mixed robotic and IoT environment. More specifically, such framework will have to support the development of service robotics applications, with robots operating in a dynamic and unknown environment, where robots behaviours are adapted and deployed at runtime.
The thesis work will consist in an initial scouting of state-of-the-art technologies (e.g. mainly OSGi standard specifications, Apache Karaf, AIOLOS, Paremus Service Fabric, Ansible, ROS). The framework will make use of novel and existing technologies and will prosecute a work under development within the BRAIN-IoT EU-funded project, in collaboration with other partners from Europe. The final outcome of this thesis proposal will consist in a proof-of-concept tested with robotic simulation environments (e.g. Gazebo, Webots) and demonstrated with real robotic platforms and IoT devices.
The developed framework will be released open source under Eclipse Foundation incubation initiatives.
The deadline for the demonstration of the developed proof-of-concept is mid December 2020.
Available Thesis: 1
Contact: send a resume with attached the list of exams taken during the Master of Science to xu.tao@linksfoundation.com or enrico.ferrera@linksfoundation.com or pert@linksfoundation.com specifying the thesis title.
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.
Thesis Code: 19022
Thesis Type: M.Sc. thesis in Machine Learning, Data Science, Computer Science, Mathematics, or equivalent
Research Area: Data Science for Industrial and Societal Application
Requirements:
- Knowledge of Python
- Software development skills
- Basic concepts on data science, concerning data analysis, processing and machine learning
- Basic concepts on image processing
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 machine learning algorithms useful for detecting surrounding vehicles and predicting their 3D bounding volumes. The proposed algorithms will be trained using open and synthetic dataset. This is created with the help of dedicated tools for extracting information from the Grand Theft Auto V graphic engine or any other simulator. The candidate will have both the task of creating the dataset and studying and evaluating the best algorithms to apply for the case of study.
The candidate is required to implement machine learning algorithms, with the exploratory possibility of deep learning algorithms using popular framework (TensorFlow, PyTorch, Keras, etc..).
Contact: send a resume with attached the list of exams to mirko.zaffaroni@linksfoundation.com specifying the thesis code and title.
Thesis code: 19021
Thesis type: M.Sc. thesis in Environment, Land and Infrastructure Engineering / M.Sc. thesis in Territorial, Urban, Environmental and Landscape Planning / equivalent
Research Area: Urban Mobility & Logistic Systems
Requirements
- Interest in transport models and planning
- Experience in data analysis
- Experience with Geographic Information Systems (GIS)
- Ability to critically interpret analytical results
Description
The diffusion of autonomous driving vehicles and their consequent impact on the urban system represent an emerging topic of research and of increasing interest in the academic, industrial and institutional fields.
This thesis project aims to investigate some issues related to the diffusion of autonomous driving vehicles in the urban context, including: (a) the development of different scenarios of autonomous vehicles diffusion, (b) the impact evaluation of different autonomous vehicles diffusion scenarios, (c) the simulation of urban traffic after a replacement of the circulating vehicle fleet with such vehicles.
The models can be tested on a case study of interest (the city of Turin) to outline possible scenarios for the public decision maker and to support urban planning processes. Based on the candidate’s skills and interests, it will be possible to carry out an in-depth analysis on transport models and / or urban planning policies.
The thesis will be carried out in collaboration between the Links Foundation (Urban Mobility & Logistic Systems Area) and the Polytechnic of Turin (Department of Environment, Land and Infrastructure Engineering / Interuniversity Department of Regional and Urban Studies and Planning).
Contacts: send a resume with attached the list of exams to maurizio.arnone@linksfoundation.com specifying the thesis code and title.
Thesis Code: 19020
Thesis Type: M.Sc. thesis in Mathematical Engineering
Research Area: Urban Mobility & Logistic Systems
Requirements
- Familiarity with differential models and partial differential equations
- Experience with main programming languages (Python, C/C++)
- Interest in Transport Planning
- Ability to critically interpret analytical results
Description
The penetration of driver-assists/autonomous vehicles and their consequent impact on traffic is an emerging, thus still under-explored, research topic in the realm of Artificial Intelligence.
The thesis will be carried out in the UML area (Urban Mobility & Logistic Sytems) of the LINKS Foundation. There will be a strict interaction with the research group of Prof. Andrea Tosin at the Department of Mathematical Sciences “G. L. Lagrange” of Politecnico di Torino.
The thesis project is grounded on three main methodological aspects: modelling, model analysis and numerical simulation, which may be more or less developed according to the student’s taste. After deepening the state of the art on methods for traffic modelling, the student will be asked to develop a mesoscopic model for simulating the flow of vehicles and to analyse alternative penetration scenarios for driver-assist/autonomous vehicles. In particular, the model will be possibly tested on a case study of interest (such as e.g., the city of Turin) using the software MATSim and discussing critically the results. The latter may be used to outline recommendations and suggestions for the public decision maker and as an input to urban planning processes.
References
- Pareschi, G. Toscani. Interacting Multiagent Systems: Kinetic Equations and Monte Carlo Methods, Oxford University Press, Oxford, UK, 2013
- Tosin, M. Zanella. Control strategies for road risk mitigation in kinetic traffic modelling, IFAC-PapersOnLine, 51(9):67-72, 2018
- Tosin, M. Zanella. Kinetic-controlled hydrodynamics for traffic models with driver-assist vehicles, Multiscale Model. Simul., 17(2):716-749, 2019
- Tosin, M. Zanella. Uncertainty damping in kinetic traffic models by driver-assist controls, preprint, 2019 (available at: http://doi.org/10.13140/RG.2.2.35871.41124)
Contacts: send a resume with attached the list of exams to maurizio.arnone@linksfoundation.com specifying the thesis code and title.
External Links: https://didattica.polito.it/pls/portal30/sviluppo.tesiv.elenchi?sdu=&idt=8030&lang=IT&opng=&opnc=
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.
Thesis Code: 19018
Thesis Type: M.Sc. thesis in Machine Learning, Data Science, Computer Science, Mathematics, or equivalent
Research Area: Data Science for Industrial and Societal Application
Requirements:
- Knowledge of Python
- Software development skills
- Basic concepts on data science, concerning data analysis, processing and machine learning
- Basic concepts on image processing
Description:
Monitoring and recognizing pedestrians’ actions is important, especially in areas where there is a risk of terrorist attacks or that are normally poorly guarded, in order to guarantee the safety of the citizens. It is therefore necessary to support the police or other surveillance agencies with intelligent systems. These systems must recognize and track an individual, in order to analyse displacements and actions taken by the target in a series of video sequence.
The objective of this thesis consists in the study and implementation of machine learning algorithms useful for recognizing and tracing a pedestrian in non-contiguous video scenes (re-identification). The proposed algorithms will be trained using a synthetic dataset. This is created with the help of dedicated tools for extracting information from the Grand Theft Auto V graphic engine. The candidate will have both the task of creating the dataset and studying and evaluating the best algorithms to apply for the case of study. The candidate is required to implement machine learning algorithms, with the exploratory possibility of deep learning algorithms using popular framework (TensorFlow, PyTorch, Keras, etc..).
Contact: send a resume with attached the list of exams to mirko.zaffaroni@linksfoundation.com specifying the thesis code and title.
Thesis Code: 19017
Thesis Type: M.Sc. thesis in Machine Learning, Data Science, Computer Science, Mathematics, or equivalent
Research Area: Data Science for Industrial and Societal Application
Requirements:
- Knowledge of Python
- Software development skills
- Basic concepts on data science, concerning data analysis, processing and machine learning
- Basic concepts on image processing
Description:
Monitoring and recognizing pedestrians’ actions is important, especially in areas where there is a risk of terrorist attacks or that are normally poorly guarded, in order to guarantee safety of the citizens. It is therefore necessary to support the police or other surveillance agencies with intelligent systems. These systems must recognize actions that are considered negative, such as theft, brawl, drug dealing, etc.
The objective of this thesis consists in the study and implementation of machine learning algorithms useful for recognizing and report negative actions performed by pedestrians in a video sequence. The proposed algorithms will be trained using a synthetic dataset and open datasets. This is created with the help of dedicated tools for extracting information from the Grand Theft Auto V graphic engine. The candidate will have both the task of creating the dataset and studying and evaluating the best algorithms to apply for the case of study. The candidate is required to implement machine learning algorithms, with the exploratory possibility of deep learning algorithms using popular framework (TensorFlow, PyTorch, Keras, etc..).
Contact: send a resume with attached the list of exams to mirko.zaffaroni@linksfoundation.com specifying the thesis code and title.
Thesis Code: 19016
Thesis Type: M.Sc. thesis in Machine Learning, Data Science, Computer Science, Mathematics, or equivalent
Research Area: Data Science for Industrial and Societal Application
Requirements:
- Knowledge of Python
- Software development skills
- Basic concepts on data science, concerning data analysis, processing and machine learning
- Basic concepts on image processing
Description:
Predicting pedestrian’s future path is important for both self-driving cars and security systems. In fact, it allows to prevent dangerous situations such as traffic accidents between cars and people or collision between autonomous robot and people. Pedestrians follow different trajectories to avoid obstacles and accommodate fellow pedestrians. Any autonomous vehicle navigating such a scene should be able to foresee the future positions of pedestrians and accordingly adjust its path to avoid collisions.
The objective of this thesis consists in the study and implementation of machine learning algorithms useful for predicting the path a pedestrian will take in the successive frames of a video sequence. The proposed algorithms will be trained using a synthetic dataset and open datasets. This is created with the help of dedicated tools for extracting information from the Grand Theft Auto V graphic engine. The candidate will have both the task of creating the dataset and studying and evaluating the best algorithms to apply for the case of study. The type of algorithms being studied and tested are to be classified among those of video analysis, with the exploratory possibility of deep learning algorithms using popular framework (TensorFlow, PyTorch, Keras, etc…).
Contact: send a resume with attached the list of exams to mirko.zaffaroni@linksfoundation.com specifying the thesis code and title.
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.
Thesis Code: 19012
Thesis Type: Computer Science, Cybersecurity, Cyber Range
Research Area: Cybersecurity
Requirements:
- Experience with Distributed Systems and Applications
- Experience with Virtualization and System Administration
- Knowledge of Defensive and Offensive Cybersecurity
- Basic knowledge of Cybersecurity Exercises (Red and Blue Teams)
- Curiosity-driven mindset
Description
A cyber range [1] is an interacting, simulated representation of real-world systems used for training IT and cybersecurity professionals, assessing incident response processes, and testing new technologies. A cyber range recreates the experience of responding to a cyber-attack by replicating the security operations center (SOC) environment, the organizational network, and the attack itself. As a result, a cyber range enables hands-on training in a controlled and secure environment.
The thesis will be structured as follows:
- state-of-the-art analysis of existing cyber ranges;
- design of a reference architecture of a cyber range;
- first small-scale implementation of the cyber range;
- testing and validation.
References
- NIST, Cyber Ranges; available at: https://www.nist.gov/sites/default/files/documents/2018/02/13/cyber_ranges.pdf
Contact: send a resume with attached the list of exams to andrea.vesco@linksfoundation.com specifying the thesis code and title.
Thesis Code: 19010
Thesis Type: Master Thesis for Computer Science, Computer Engineering, Electronic Engineering, Physic Engineering
Research Area: Advanced Computing & Applications
Requirements
- MS students in Electronic Engineering, Computer Science, Computer Engineering, Physic Engineering
- Experience with main programming languages (Python, C/C++), algorithms
Description
The pace at which silicon-based computer architectures are evolving is making transistors’ size reaching physical limits. However, to continue increasing processing capabilities, other approaches are researched. Among the others, Quantum Computing technologies represent a fully disruptive departure from traditional way of thinking computer architectures and their algorithms. Quantum computers are expected to solve large complex problems that are not addressable with current and future supercomputers. Many Quantum Algorithms (QAs) have the potential of exponential speed-up compared to their classical counterpart and are thus of primary interest for exploiting the capability of future quantum machines.
The objective of the work is to study the applicability of Quantum Algorithms (e.g., Shor, quantum Fourier Transform – QFT, Grove, etc.) to complex and relevant problems we are facing in scientific and engineering fields (e.g., bioinformatics, optimization problems, etc.). The thesis work will be oriented on using IBM QX and D-Wave platforms, although further platforms will also be considered.
Contact: Send CV to alberto.scionti@linksfoundation.com and olivier.terzo@linksfoundation.com specifying the thesis code and title.
Thesis Code: 19009
Thesis Type: Master Thesis for Computer Engineering, Electronic Engineering
Research Area: Advanced Computing & Applications
Requirements
- MS students in Electronic Engineering, Computer Engineering
- Experience with main programming languages (Python, C/C++, Go), algorithms
Description
The pace at which Cloud based services are adopted is pushing Cloud service providers (CSPs) to adopt more heterogeneous resources in their data centers. Computing resource diversification is pushed also by the growing adoption of machine learning and deep learning algorithms (also HPC applications are going in this direction), which generally require specialized hardware to efficiently execute. Managing resources and tasks (i.e., deciding an allocation of the tasks under a certain set of constraints) at large scale requires innovative scheduling techniques. However, current schedulers still rely on simple strategies to assign tasks to available resources.
The objective of the work is to study innovative approaches for managing resources in Cloud-HPC environments. To this end, machine learning and evolutionary based techniques will be considered for improving the quality of task scheduling when heterogeneous resources are available (e.g., GPUs, FPGAs, dedicated ASICs). Studied techniques will look at improving scheduling under different constraints (energy saving, reducing task makespan, etc.) and combination of them. Targeted technologies include Linux containers and related orchestrators such as Kubernetes.
Contact: Send CV to alberto.scionti@linksfoundation.com and olivier.terzo@linksfoundation.com specifying the thesis code and title.
Thesis Type: Thesis in Computer Science, Data Engineering, Computer Engineering, Mathematical Engineering, Data Science
Research Area: Innovation Development
Requirements:
• Experience with Python and/or Java and/or Node.js
• Basic knowledge of modular development
• Beginner of (or willing to learn quickly) deep learning and natural language processing
• Curiosity-driven mindset.
Description
Public procurement contracts are a rich source of knowledge necessary for seizing the efforts in participating to public procurement calls. However, contracts are usually available in textual format making harder the task of extracting structured information automatically and being used in automated systems. This thesis will focus on extracting structured information from those documents such as specific dates, unique identifiers (VAT id, protocol numbers, telephone number), named entities (places, people, business entities, products). In this thesis the undergraduate will study and experiment with deep learning and natural language processing techniques that are the core of the Artificial Intelligence stack, by understanding the intrinsic semantics of document and identifying and linking pivotal information found in the text to an external database.
The thesis will be structured as follows:
• state-of-the-art analysis of text processing techniques
• problem formulation: objective function, data structures and resources to be used
• algorithm design and prototyping
• in-lab testing verification with real data and measurement of the performance of the approach.
The thesis will be co-tutored with Synapta Srl, a Spin-off of Politecnico di Torino. It will be an opportunity to work also with the Synapta team experimenting with real data. The undergraduate will benefit from being immersed in a existing start-up environment while applying scientific experimental practises learned in ISMB. At the end of the thesis, the undergraduate will be familiar with deep learning and natural language processing techniques, and she/he will acquire an understanding of the public-procurement domain. As additional benefit, she/he will proficiently use control version systems, continuous integration systems, remote deploying and monitoring techniques.
Contact: send a resume with attached the list of exams to giuseppe.rizzo@linksfoundation.com specifying the thesis code and title.
Thesis Type: Thesis in Computer Science, Data Engineering, Computer Engineering, Mathematical Engineering, Data Science
Research Area: Innovation Development
Requirements:
• Experience with Python and/or Java and/or Node.js
• Basic knowledge of modular development
• Beginner of (or willing to learn quickly) machine learning and natural language processing
• Curiosity-driven mindset
Description
Italian public administration websites contain a lot of resources published as open data. However, administrations have multiple websites and each has its own semantic structure making harder to autonomous crawlers retrieving the necessary information. The aim of this thesis project is to develop an intelligent data crawler able to fetch specific types of resources across multiple file formats from selected sources. Relatively low precision and high recall is expected. The crawler should be able to detect relevant resources using state-of-the-art techniques based on machine learning and natural language processing techniques that are the core of the Artificial Intelligence stack.
The undergraduate will study and experiment with technologies for:
• extracting semantics from web resources
• understanding the content
• discriminating about the value of the retrieved content.
The thesis will be structured as follows:
• state-of-the-art analysis of information retrieval
• problem formulation: objective function, data structures and resources to be used
• algorithm design and prototyping
• in-lab testing verification with real data and measurement of the performance of the approach.
The thesis will be co-tutored with Synapta Srl, a Spin-off of Politecnico di Torino. It will be an opportunity to work also with the Synapta team experimenting with real data. The undergraduate will benefit from being immersed in a existing start-up environment while applying scientific experimental practises learned in ISMB. At the end of the thesis, the undergraduate will be familiar with machine learning and natural language processing techniques, and she/he will acquire an understanding of the public-procurement domain. As additional benefit, she/he will proficiently use control version systems, continuous integration systems, remote deploying and monitoring techniques.
Contact: send a resume with attached the list of exams to giuseppe.rizzo@linksfoundation.com specifying the thesis code and title.
Thesis Type: Master Thesis for Computer Science or Computer Engineering students or equivalent
Research Area: Pervasive Technologies
Required Skills:
• Good written English level
• Java, C++ or equivalent Object-Oriented language
• Version control system (GIT, SVN)
• Proactive mindset, problem-solving oriented
• Python or equivalent scripting languages will be a plus
Description
Motivation: One of the hottest topics of the last century is the environment preservation. Particularly, over the last years, the attention of our society has been focused, in the industrial context, on the need of reducing both waste production and toxic emissions. Internet-Of-Things (IOT) has become a well-established reality over the past years: by introducing very advanced devices and exposing heterogeneous service it has been used in various scenarios and areas of interest, i.e. the industry domain. IOT devices have already been adopted in factories, contributing to a more sustainable production. Such an interest adoption of IOT solutions has attracted ISMB’s interest, leading to the investigation on how Multi Agent Systems can be used in conjunction with IOT technologies to pursue the objectives previously described. A multi-agent system is a computerized system composed of multiple interacting intelligent agents, thought for resolving problems that would result difficult or impossible to solve for an individual agent. MAS can be used in the most disparate domains, ranging from Distributed Constraints Optimization (DCO) problems to coordination and delegation of computational tasks.
Objectives: This thesis will focus on the analysis and development of a Multi-Agent System (MAS) interacting with an IOT system, providing asynchronous data in a Fabric-Of-the-Future context (e.g. waste management collection and dispatching). After a state of the art survey of MAS, the student will build a system in either a collaborative or antagonist scenario, exploiting standard communication protocols (such as AMQP, MQTT, etc.). Furthermore, the student will analyze existing algorithms and interaction protocols (e.g. consensus algorithms, Vickrey auctions, etc.) in order to solve complex distributed problems such as searching the Pareto optimality in a virtual marketplace scenario.
Contact: send a resume with attached the list of exams to claudio.pastrone@linksfoundation.com or giuseppe.pacelli@linksfoundation.com specifying the thesis code and title.
Thesis Type: Thesis in Computer Science, Data Engineering, Computer Engineering, Mathematical Engineering, Data Science
Research Area: Innovation Development
Requirements
• Experience with Python and/or Java
• Basic knowledge of modular development
• Beginner of (or willing to learn quickly) machine learning
• Curiosity-driven mindset.
Description
The digital transformation that healthcare has undergone has encouraged the generation of a large quantity of digital clinical notes. Majority of those notes contain unstructured information which complicates the search, analysis, and the understanding of the content. The automated analysis and understanding of those notes is of now one of the biggest challenges in healthcare.
In this thesis the undergraduate will study and experiment with AI-based technologies for:
• extracting and classify key information such as adverse events from clinical notes written in natural language;
• generating a coherent and human-readable summary of a sequence of clinical notes.
The thesis will be structured as follows:
• state-of-the-art critical analysis in the field of artificial intelligence applied to healthcare;
• problem formulation: objective function, data structures and resources to be used;
• algorithm design and prototyping;
• in-lab testing verification with real data and measurement of the performance of the approach.
The thesis will be co-tutored with the Institute of Biomedical Engineering, University of Oxford. As the opportunity arises, there could be the possibility of doing this thesis abroad depending on the requirements and plans of the master you are enrolled in. The undergraduate will benefit from being immersed in a research environment. It is a unique setting to get into a research mindset with a strong push for innovation. At the end of the thesis, the undergraduate will be familiar with deep learning and semantic analysis, and he will acquire an understanding of the healthcare domain. During the project, he will be able to design and implement an intelligent system applied to real case studies. As additional benefit, she/he will use proficiently control version systems, continuous integration systems, remote deploying and monitoring techniques.
Contact: Send a resume with attached the list of exams to giuseppe.rizzo@linksfoundation.com specifying the thesis code and title.
Thesis Type: Thesis in Computer Science, Data Engineering, Computer Engineering, Mathematical Engineering, Data Science
Research Area: Innovation Development
Requirements
• Experience with Python and/or Java
• Basic knowledge of modular development
• Beginner of (or willing to learn quickly) machine learning
• Curiosity-driven mindset.
Description
Automated assistants are now more than ever taking place in our daily life. Assistants are thus asked to generate content according to user’ inputs and contextual objectives. Let take the case of a scientist in his daily task of performing experiments, filling tables and reporting findings. Lots of his time is spent in transcribing findings that have been already elaborated and encoded in tables. The advancements achieved in artificial intelligence support scenarios of co-operation between an artificial intelligence-based assistant and a scientist when writing technical reports. The objective of this thesis will be thus researching and prototyping an intelligent system able to write science starting from tables. In this thesis the undergraduate will develop an AI-based system for writing scientific papers using both semantic analysis and deep learning. The system will be able to learn autonomously from pairs of tables and papers created as gold examples and generate from a newer table a report.
The thesis will be structured as follows:
• state-of-the-art critical analysis in the field of document generation using both semantic analysis and deep learning;
• problem formulation: objective function, data structures and resources to be used;
• algorithm design and prototyping;
• in-lab testing verification with real data and measurement of the goodness of the approach.
The undergraduate will benefit from being immersed in a research environment. It’s a unique setting to get into a research mindset with a strong push for innovation. At the end of the thesis the undergraduate will be familiar with semantic analysis and deep learning and be able to implement an intelligent system. As additional benefit, she/he will use proficiently control version systems, continuous integration systems, remote deploying and monitoring techniques.
Contact: Send a resume with attached the list of exams to giuseppe.rizzo@linksfoundation.com specifying the thesis code and title.
Thesis Type: Thesis in Computer Science, Data Engineering, Computer Engineering, Mathematical Engineering, Data Science
Research Area: Innovation Development
Requirements
• Experience with Python and/or Java
• Basic knowledge of modular development
• Beginner of (or willing to learn quickly) machine learning
• Curiosity-driven mindset.
Description
A critical pain of all organizations is managing competencies of their personnel due to both internal variations of topic characterizations (usually an employee acquires knowledge and evolves his professional competence spectrum) and external towards aligning new trends and market requirements. In this thesis the undergraduate will develop an intelligent system for compressing scientific papers into a list of topics in a lossy manner using both semantic analysis and deep learning. The system will be able to learn autonomously from a set of scientific papers authored by scholars and be able to characterize an unknown scholar starting from her/his set of scientific articles.
The thesis will be structured as follows:
• state-of-the-art critical analysis in the field of document summarization using both semantic analysis and deep learning;
• problem formulation: objective function, data structures and resources to be used;
• algorithm design and prototyping;
• in-lab testing verification with real data and measurement of the goodness of the approach.
The undergraduate will benefit from being immersed in a research environment. It’s a unique setting to get into a research mindset with a strong push for innovation. At the end of the thesis the undergraduate will be familiar with deep learning and semantic analysis and be able to implement an intelligent system. As additional benefit, she/he will use proficiently control version systems, continuous integration systems, remote deploying and monitoring techniques.
Contact: Send a resume with attached the list of exams to giuseppe.rizzo@linksfoundation.com specifying the thesis coda and title.
Thesis Type: 6 months Master Thesis (Laurea Magistrale) for students of: Computer Engineering, Communications and ICT Engineering, Mathematical Engineering or equivalent.
Research Area: Pervasive Technologies
Requirements:
– Skills in algorithms development and programming
– Interest for industrial applications.
Description:
Motivation:
The proposed work originates from two facts:
• Predictive control in industry could produce significant economical and environmental advantages. However, the industrial processes are generally very complex to model and control.
• Machine Learning has been proved to deal with complex systems and large datasets very efficiently. It is able to model input-output systems with very few model constraints.
It is then natural to think to the implementation of Machine Learning techniques to control and optimize industrial processes. Even though very popular for a variety of technological services, Machine Learning has also a great potential in the industrial domain, which has not been fully investigated yet. The interest on this topic is growing rapidly both from the industrial and scientific sectors.
Objective:
The purpose of this thesis is to study and develop Machine Learning algorithms and implement them to tackle industrial problems. A theoretical in-depth analysis of Machine Learning techniques will be the starting point of the work. Afterwards, the implementation of suitable algorithms will be tested on industrial datasets, with the purpose of exploiting time series data to predict failures and/or classify low-quality products.
Contacts: send a resume specifying the thesis code and title to claudio.pastrone@linksfoundation.com
Requirements:
• MS students in Electronic Engineering/Computer Science or equivalent
• Good attitude towards the understanding of emerging trends
• Basic knowledge of network protocols and tools for data analysis
Description:
Deep learning is an emerging branch of machine learning based on the study of algorithms that attempt to model behaviors (e.g. actions of a user) to predict, analyze and propose optimal strategies to solve problems in an automated way e.g. by adopting neural networks. The objective of this work is to carefully study the literature and the state-of-the-art on this topic together with the tools and framework actually proposed in this field (e.g. Google TensorFlow, Caffe , Torch…)
Contacts: Send CV to daniele.brevi@linksfoundation.com specifying the thesis code and title
Requirements:
● MS students in Electronic Engineering/Computer Science or equivalent
● Experience with main programming languages (C/C++)
● Good knowledge of image processing techniques, network protocols
Description:
The objective of the thesis will be to study and implement innovative and effective tools to help car drivers. The state-of-the-art in this area already includes applications such as road signs and lane detectors. New and promising applications will be studied and tested in real scenarios (finding free parking slots, interacting with neighbors…).
Contacts: Send CV to daniele.brevi@linksfoundation.com specifying the thesis code and title
Requirements:
● MS students in Electronic Engineering/Computer Science or equivalent
● Experience with main programming languages (C/C++)
● Good knowledge of image processing techniques, network protocols
Description:
Acronyms like H.265, VP9, MMT, DASH, WebRTC, UPNP 2.0 are becoming familiar in these years as new and promising standards for video encoding and multimedia transmission. The objective of the work will be defined more in details when the thesis will be carried out according to the most attractive technologies at the moment and the student’s interests. As an example, tests using the software verification model of the new H.265/HEVC video codec in different scenarios can be performed and also its extensions for future applications can be analyzed.
Contacts: Send CV to daniele.brevi@linksfoundation.com specifying the thesis code and title
Requirements:
• MS students in Electronic Engineering/Computer Science or equivalent
• Experience with main programming languages (C/C++)
• Basic knowledge of video processing and transmission techniques
Description:
Unmanned Aerial Systems equipped with video cameras usually require a significant bandwidth to transmit in real-time data to the ground for live analysis and mission control. State-of-the-art mobile infrastructures (3G, 4G…) nowadays offer a very time-variant channel to transmit such information so the adaptation of the video stream is a desired feature. The objective of this thesis is to study a framework able to perform an on-board live H.264/AVC video encoding by means of dedicated HW boards. Different configurations and solutions will be analyzed to gather information about performance and complexity of such a scheme. Furthermore, on-demand adaptive streaming will be studied and implemented in order to adapt the data rate to the actual channel state.
Contacts: Send CV to daniele.brevi@linksfoundation.com specifying the thesis code and title
Requirements:
● MS students in Electronic Engineering/Computer Science or equivalent
● Experience with main programming languages (C/C++)
● Good knowledge of image processing techniques
Description:
Unmanned Aerial Systems equipped with HD cameras and a plethora of sensors are becoming popular in recent years. A wide range of applications is then expected to gain attention by exploiting image processing algorithms. The objective of this work is to discuss and implement innovative features for video concealment in case any losses occur during data transmission to the base station.
Contacts: Send CV to daniele.brevi@linksfoundation.com specifying the thesis code and title
Requirements:
● MS students in Electronic Engineering/Computer Science or equivalent
● Experience with main programming languages (C/C++)
● Good knowledge of image processing techniques
Description:
The new MPEG Compact Descriptor for Visual Search (CDVS) standard allows to significantly compress the key-points of an image in order to allow the processor to deal with much less information thus efficiently perform data analysis and matching. Deriving the key-point for an image allows comparison, storage and retrieval in a faster way in image-based applications where speed and efficiency are required features. The objective of this work is to explore how this new standard can improve biometric analysis. The student will study the framework of MPEG CDVS to understand how the keypoints for biometric images are generated. As a possible application, images of fingerprints and/or retina will be analyzed and an architecture encompassing fingerprint matching will be developed.
Contacts: Send CV to daniele.brevi@linksfoundation.com specifying the thesis code and title
Requirements:
• MS students in Electronic Engineering/Computer Science or equivalent
• Experience with MatLab
• Good knowledge of image processing techniques
Description:
Image processing can dramatically help the recognition of a user’s vital sign (heart rate, respiratory rate…) by processing the so-called micro-movement of the body. This subtle motion can be study by acquiring video data thus deriving significant information for clinical assessment without the need of a contact with the user. The objective of this work is to study the already implemented algorithms in this emerging field, and modify them in order to fit them to heterogeneous scenarios and test the performance.
Contacts: Send CV to daniele.brevi@linksfoundation.com specifying the thesis code and title
