Haptic feedback is an essential aspect of human-computer interaction, particularly in industrial settings where precise and realistic touch sensations are crucial for successful manual tasks. Collaborative robots (Cobots) have revolutionized industrial production processes by working alongside human operators, and this characteristic also opened up other uses for such devices, for example, in the context of Virtual Reality (VR). This thesis proposal aims to investigate the feasibility of providing faithful on-demand haptic feedback using collaborative robots as "Encountered Type" haptic devices, to simulate a manual task (i.e., defects identification and correction) within industrial production processes. The research will also explore how this technology can enhance defect identification, issue management, and improve workplace safety by simulating ergonomic aspects and delivering effective training content. The investigation will be conducted in collaboration with industrial partners and will align with the objectives of the MANAGE 5.0 project, supported by the Ministry of Enterprises and Made in Italy (MISE) and the National Recovery and Resilience Plan (PNRR).

Haptic feedback is an essential aspect of human-computer interaction, particularly in industrial settings where precise and realistic touch sensations are crucial for successful manual tasks. Collaborative robots (Cobots) have revolutionized industrial production processes by working alongside human operators, and this characteristic also opened up other uses for such devices, for example, in the context of ...

In modern production processes, the efficient detection and management of defects are critical for ensuring high-quality output and optimizing manufacturing operations. The advent of automation and advanced computer vision techniques has enabled the automatic detection of defects in real-time. However, conveying this vital information to production line operators in a clear, intuitive, and timely manner remains a challenge. This thesis proposal aims to investigate the application of Augmented Reality (AR) technology, enhanced by Collaborative Robot (Cobot) assistance, as a means of conveying automatically detected defects information to production line operators. The research will involve the integration of the output from computer vision algorithms and defect detection methodologies within a production line. Cobots will be instrumental in assisting operators during the tasks of defect detection and correction. By utilizing AR technology, the proposed investigation seeks to overlay defect-related information, such as visual cues, guidance, and instructions, directly onto the physical production environment in real-time, with the Cobot playing a pivotal role in enhancing the operator's capabilities and accuracy during defect management tasks. This approach aims to enhance operators' situational awareness, enable rapid decision-making, and facilitate timely defect resolution. The research collaboration will actively engage industry partners, ensuring the practicality and relevance of the findings. Moreover, the project will be in alignment with the overarching objectives of the MANAGE 5.0 project, with support from the Ministry of Enterprises and Made in Italy (MISE) and the National Recovery and Resilience Plan (PNRR).

In modern production processes, the efficient detection and management of defects are critical for ensuring high-quality output and optimizing manufacturing operations. The advent of automation and advanced computer vision techniques has enabled the automatic detection of defects in real-time. However, conveying this vital information to production line operators in a clear, intuitive, and timely manner remains ...

Several thesis proposals are available in the context of the "Centro Nazionale Mobilità Sostenibile" ("National Center for Sustainable Mobility"), in particular of the project "Air mobility" funded by the National Recovery and Resilience Plan (PNRR). These proposal will concern the design and development of virtual simulations and human-computer interfaces for advanced air mobility, and could be developed in collaboration with the other project partners.

One of these proposals will concern the creation of a simulation environment for flying vehicles. Starting from existing software, an immersive environment capable to simulate, both visually and physically, the cabin/cockpit of a Vertical Take-Off and Landing (VTOL) aircraft (manned or un-manned) will be created. The physical simulation will leverage a motion platform available at VR@POLITO. Typical tasks for VTOL aircrafts like passenger transport, emergency medical services, or search and rescue (SAR) will be considered.

Another proposal strictly related to the previous one will concern the design and evaluation of interfaces capable to enhance the situational awareness of pilots of manned aircrafts. In particular, since the two main human-vehicle communication channels, i.e., the visual and aural ones, are typically overloaded, the aim is to investigate how to leverage the haptic channel. In particular, by using a commercial haptic suit, the plan is to investigate the effectiveness of a multi-sensory stimulation combining the above channels with the haptic one by means of full-body touch stimuli.

Another proposal will concern the use of spatial audio in immersive VR simulations to investigate the acoustic experience in flying vehicles. Though a collaboration with the DIMEAS department, acoustic simulations of the the vehicle's mechanical structure and motors will be generated. By means of a VR simulation, users will be immersed a synthetic environment and will be allowed to evaluate different configurations based on their acoustic impact.

Several thesis proposals are available in the context of the "Centro Nazionale Mobilità Sostenibile" ("National Center for Sustainable Mobility"), in particular of the project "Air mobility" funded by the National Recovery and Resilience Plan (PNRR). These proposal will concern the design and development of virtual simulations and human-computer interfaces for advanced air mobility, and could be developed in ...

Finding educational material that is required to complete the own set of possessed skills or those of a company's workforce is essential in today's knowledge society.

The IEEE Digital Reality has developed a tool, known as Knowledge as a Service or KaaS (https://kaas.ieee.org/) that can be used to describe a set of possessed skills based on the IEEE ontology and match it against the knowledge base of IEEE Technology Navigator, thus gaining access to the universe of information related to IEEE conferences, publications, standards, and articles.

The aim of this thesis is to create a virtual environment for letting users access the KaaS tool in an engaging manner.

It will be necessary to choose whether to use one of the existing VR-enabled Metaverse or Social VR platforms, or develop an hoc one, e.g., starting from a custom virtual environment recently developed by the GRAINS group and the VR@POLITO for similar purposes. A pleasant and appropriate immersive space will have to be created. Then, the virtual environment will have to be connected to KaaS using available REST APIs, and an effective (e.g., gamified) user interface will have to be developed.

Finding educational material that is required to complete the own set of possessed skills or those of a company's workforce is essential in today's knowledge society.

The IEEE Digital Reality has developed a tool, known as Knowledge as a Service or KaaS (https://kaas.ieee.org/) that can be used to describe a set of possessed skills based on the IEEE ontology and match it against the knowledge base ...

The recognition of the vehicle’s owner or the person authorized to operate a given vehicle will be a challenge for future intelligent vehicle applications. Furthermore, the person to be recognized stands outside the car, in different and complex conditions (recognition in the “wild”): highly variable lighting, presence of other subjects, etc. Apart from the difficulty and complexity of the recognition algorithm, one of the fundamental problems becomes the data needed to train required AI models, which cannot be available yet.

In this case, so-called synthetic data need to be employed. Synthetic data, generated through some simulation environments, can mimic operational conditions, by also enabling to keep edge cases into account. In fact, real-world datasets often contain imbalances, because edge cases, which do not happen frequently in real life, are not sufficiently represented. Finally, with synthetic data, other issues, e.g., related to privacy and GDPR compliance, can be easily overcome.

The focus of this thesis, developed in collaboration with Centro Ricerche Fiat (CRF) and Stellantis, will be the use of computer graphics simulation for the the generation of synthetic data that could be later used to train AI algorithms for the recognition of vehicle owners that are robust to variations in terms of, e.g., illumination, presence of other human and non-human subjects, etc.

The recognition of the vehicle’s owner or the person authorized to operate a given vehicle will be a challenge for future intelligent vehicle applications. Furthermore, the person to be recognized stands outside the car, in different and complex conditions (recognition in the “wild”): highly variable lighting, presence of other subjects, etc. Apart from the difficulty and complexity of the recognition ...

Virtual humans (or digital humans) are simulations of human beings on computers, which are considered today a kind of commodity in movies, video games and the entertainment domain in general. In fact, virtual humans are getting commonplace in various industries and domains, from medicine, to film, fashion, education, automobile, telecommunications, etc. Depending on the application, various level of realisms might be needed.

A medical application requires an exact simulation of specific internal organs or of human emotional states; an education or a cultural heritage application requires effective communication/storytelling and interaction abilities to effectively deliver the intended contents and manage dialogues; the film industry requires extremely pleasant appearances, as well as natural movements and expressions; ergonomic studies, like clothing applications, require faithful body proportions, realistic locomotion, etc.; the metaverse require social behavior to be replicated in a credible way via easy-to-use interfaces.

The research domain of virtual humans is concerned with their representation, movement and behavior, and is addressing related challenges through a multi-disciplinary approach that encompasses, among others, computer graphics, computer animation, computer vision and artificial intelligence.

The aim of this thesis is to explore possible methods and tools for the creation and the control of intelligent virtual humans that can be used in different projects currently ongoing at the VR@POLITO lab.

In particular, activities could concern:

- the creation of digital avatars for virtual visits being created for cultural institutions in Piemonte and in Italy (in collaboration with DAD and DISEG departments);
- the development of empathic avatars of patient and doctors to support training in the medical domain in collaboration with OntarioTech university (Ontario, Canada);
- the implementation of conversational agents that can be used in customer service-oriented applications;
- the comparison of consumer technologies that can be exploited to enable a smooth transfer of the user's movements (body posture and gait, as well as facial expression) onto his or her virtual avatar.

Depending on the domain, the thesis can be carried out either in collaboration with a company, as a thesis at Politecnico di Torino or even as a thesis abroad in a foreign university.

Virtual humans (or digital humans) are simulations of human beings on computers, which are considered today a kind of commodity in movies, video games and the entertainment domain in general. In fact, virtual humans are getting commonplace in various industries and domains, from medicine, to film, fashion, education, automobile, telecommunications, etc. Depending on the application, various level of realisms ...

Autonomous vehicles are routinely advertized as the world-changing invention that will greatly impact people lives by, on the one hand, improving safety, reducing fuel consumption and decreasing urban infra-structure strains, and, on the other hand, freeing up huge amounts of time for their occupants, who will be allowed to read, consume, work, play, etc. Despite the enormous business opportunities, autonomous vehicles also come with some key issues.

One of the major issues is related to the fact that, in oder to use these technology, people will first have to trust it. Besides education and experience, a factor that is regarded as the possible keystone for constructing trust towards autonomous vehicles is represented by HMI, i.e., by the paradigms chosen to communicate with the users (the vehicle occupants, but also the other road users) and to make them interact with it.

At present, since just a few vehicles are actually available for real-world experiments (and not at the highest levels of autonomy envisaged by SAE, yet) the only way to design and validate approaches for possibly coping with issues like those above is through simulation.

The VR@POLITO has developed an immersive VR driving simulation platform that has been used, e.g.,

- to explore in-vehicle interfaces based on AR Head Up Displays for enhancing drivers'/passengers' trust;
- to study pedestrians' behavior at crosswalks with incoming vehicles mounting different types of signaling interfaces;
- to investigate the effectiveness of multi-modal interfaces in urban, suburban and highway drives with autonomous vehicles.

Several thesis works are available in this field, which can address the open problems from different perspectives, possibly in collaboration with a company.

Suggested readings:

Building Trust in Autonomous Vehicles: Role of Virtual Reality Driving Simulators in HMI Design
https://arxiv.org/abs/2007.13371

Comparing State-of-the-Art and Emerging Augmented Reality Interfaces for Autonomous Vehicle-to-Pedestrian Communication
https://arxiv.org/abs/2102.02783

Autonomous vehicles are routinely advertized as the world-changing invention that will greatly impact people lives by, on the one hand, improving safety, reducing fuel consumption and decreasing urban infra-structure strains, and, on the other hand, freeing up huge amounts of time for their occupants, who will be allowed to read, consume, work, play, etc. Despite the enormous business opportunities, autonomous ...

Several collaborations are in place with various institutions (Italian Airforce, Piedmont Region Civil Protection and Forest Firefighting Unit, Frejus Tunnel Authority, Fire and Rescue Department of the Savoy Region, France, etc.) involved in emergency management. In this context, a number of serious games and interactive experiences leveraging Virtual Reality, Augmented Reality and Mixed Reality have been developed to support education and training of first responders, volunteers, students, etc.

Three thesis proposals are available, to be developed mostly at Politecnico di Torino in collaboration with the said institutions.

1) The first thesis proposal aims at extending several previous thesis works that have created a prototype strategic planning and debriefing tool for forest firefighting based on a sand table. The prototype already combines a fire simulator with AR projection and mobile app-based interaction. Planned extension envisage, e.g., the integration of depth scan reconstruction of the sand construction, and the use of wearable AR headsets (HoloLens) for collaborative discussion.

2) The second thesis's gol is to build a multi-layer, multi-player training tool by integrating a series of previously-developed interactive experiences. For instance, immersive VR-based applications for training on the use of low-flame firefighting tools or high-pressure pumping equipment have been created already. The idea would be to make it possible for decision makers to see the effects of various teams operating in such applications from a kind of centralized control room.

3) The third thesis will focus on the evolution of a VR game based on a "travel-in-time" dynamics created to teach middle school students the correct procedures to follow in emergency situations. So far, hydrogeological risks (precisely floods) have been addressed. Other risks will be considered now.

In all the thesis works, experiments with end-users will be carried out to validate the effectiveness of devised solutions.

Several collaborations are in place with various institutions (Italian Airforce, Piedmont Region Civil Protection and Forest Firefighting Unit, Frejus Tunnel Authority, Fire and Rescue Department of the Savoy Region, France, etc.) involved in emergency management. In this context, a number of serious games and interactive experiences leveraging Virtual Reality, Augmented Reality and Mixed Reality have been ...

This thesis, proposed in collaboration with LINKS Foundation, will aim at the design and development of multi-player scenarios for the training of policemen by leveraging the most recent VR devices on the market (Meta Quest Pro, Meta Quest 3, Pico 4, etc.) and maximizing the advantages provided by their technical features (pancake lens, inside-out tracking, eye tracking, etc.)

This thesis, proposed in collaboration with LINKS Foundation, will aim at the design and development of multi-player scenarios for the training of policemen by leveraging the most recent VR devices on the market (Meta Quest Pro, Meta Quest 3, Pico 4, etc.) and maximizing the advantages provided by their technical features (pancake lens, inside-out tracking, eye tracking, etc.)

A "virtual laboratory" is defined as an interactive environment that enables a group of researchers located around the world to work together for creating and conducting simulated experiments. Virtual laboratories are receiving an increasing attention also by the world of education, as they could represent the practical side of classes that are implemented today according to the e-learning paradigm. The latter statement has become particularly relevant especially during the lockdown phase, and the potential of this technology could be finally unleashed now that person and distance education have started to be delivered at the same time in a hybrid mode.

In this context, several thesis proposals are available that are aimed to create multiple virtual laboratory experiences supporting, among others, the educational activities at Politecnico di Torino. Contacts have been activated with colleagues in various Departments interested in developing learning tools in the field of physics, chemistry, math, etc. that can be accessed by students through both Virtual Reality (VR) and Augmented Reality (AR) technologies. Various directions could be investigated, encompassing online and offline contents, self-learning modules, teacher-student and student-student collaboration modalities, etc.

An example of such tools developed, e.g., for the Department of Energy in the context of electrical engineering is presented in the paper below.

Immersive Virtual Reality for procedural training: Comparing traditional and learning by teaching approaches
Computers in Industry, January 2023
https://dx.doi.org/10.1016/j.compind.2022.103785

A "virtual laboratory" is defined as an interactive environment that enables a group of researchers located around the world to work together for creating and conducting simulated experiments. Virtual laboratories are receiving an increasing attention also by the world of education, as they could represent the practical side of classes that are implemented today according to the e-learning paradigm. The latter ...

Cyber-sickness is a disorder characterized by symptoms like nausea or discomfort, that can happen meanwhile or after using Virtual Reality (VR) technologies. Similarly to motion sickness, it is mainly caused by inconsistency between visual and aural stimuli from the simulated virtual environment and the expected feedback from the vestibular system. Several mitigation and prevention techniques have been proposed and adopted in many commercial products. However, most of them are highly situational, or require implementation choices that have a negative impact on core aspects such immersion, naturalness, and sense of presence.

In order to support a comprehensive evaluation and fair comparison of such techniques, a testbed including a set of four immersive scenarios able to stimulate different, controlled levels of cyber-sickness has been developed in a previous thesis.

The aim of this new thesis will be to study the state-of-the-art of cyber-sickness mitigation and prevention techniques by implementing them in the above testbed, and to possibly design new and more effective strategies for addressing this major issue of immersive Virtual Reality.

Cyber-sickness is a disorder characterized by symptoms like nausea or discomfort, that can happen meanwhile or after using Virtual Reality (VR) technologies. Similarly to motion sickness, it is mainly caused by inconsistency between visual and aural stimuli from the simulated virtual environment and the expected feedback from the vestibular system. Several mitigation and prevention techniques have been proposed ...

Generating computer animations, particularly of virtual characters, is a very labor-intensive task, which requires animators to operate with sophisticated interfaces. Hence, researchers continuously experiment with alternative interaction paradigms that could possibly ease the above task. Among others, the use of Virtual represents a valid alternative to traditional interfaces (based on mouse and keyboards) since it can make interactions more expressive and intuitive. However, although the literature proposes several solutions leveraging VR to solve different computer graphics challenges, generally these solutions are not fully integrated into the computer animation pipeline or were not designed to support collaborative working. Moreover, most of the existing solutions are still based on the common hand controllers, even though the literature has already shown the benefits provided by alternatives such as sketch-based interfaces [1].

The objective of this thesis proposal (more than one student will be accepted) is to design and develop an immersive and collaborative animation system that allows animators to produce and edit 3D character animations considering alternative interfaces. The system could be also featured with intelligent mechanisms able to support the animator in the creation of the virtual character animations, e.g., to automatically generate the next/previous frame given a current character pose. Related works have been proposed in the literature. For example, in [2] a system was proposed by the Disney research lab to support collaborative animations through a mixed reality system. Additional examples for authoring contents, 3D modeling, or directing actors in virtual scenes have been proposed in [3-5], however, according to a recent survey [6], it was not possible to identify works that make use of immersive and collaborative environments to produce computer animations. This research gap is probably due to the number of challenges that researchers have to face when approaching the design of collaborative AR/VR systems [7].

Related works:

[1] Cannavò, A., Zhang, C., Wang, W., Lamberti, F. (2020, October). Posing 3D Characters in Virtual Reality Through In-the-Air Sketches. In International Conference on Computer Animation and Social Agents (pp. 51-61). Springer, Cham.

[2] Pan, Ye, and Kenny Mitchell (Disney Research). "PoseMMR: a collaborative mixed reality authoring tool for character animation." 2020 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW). IEEE, 2020

[3] Nebeling, Michael, et al. "Xrdirector: A role-based collaborative immersive authoring system." Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. 2020.

[4] Larpkiattaworn, Noppasorn, et al. "Multiuser Virtual Reality for Designing and Editing 3D Models." International Conference on Human-Computer Interaction. Springer, Cham, 2020.

[5] Wang, Cheng Yao, et al. "VideoPoseVR: authoring virtual reality character animations with online videos." Proceedings of the ACM on Human-Computer Interaction 6.ISS (2022): 448-467.

[6] Schäfer, Alexander, Gerd Reis, and Didier Stricker. "A Survey on Synchronous Augmented, Virtual and Mixed Reality Remote Collaboration Systems." ACM Computing Surveys (CSUR) (2021).

[7] Krauß, Veronika, et al. "Current practices, challenges, and design implications for collaborative AR/VR application development." Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems. 2021.

Generating computer animations, particularly of virtual characters, is a very labor-intensive task, which requires animators to operate with sophisticated interfaces. Hence, researchers continuously experiment with alternative interaction paradigms that could possibly ease the above task. Among others, the use of Virtual represents a valid alternative to traditional interfaces (based on mouse and keyboards) ...

Extended Reality (XR) technologies, encompassing Virtual (VR), Augmented (AR) and Mixed (MR) Reality, are disrupting the way films are produced and viewed. Although these technologies are getting commonplace, they are presenting new, unexplored challenges to both the actors and final viewers. For instance, when motion capture (mocap) is used to record actors’ movements with the aim of animating digital characters, an increase in the workload is observed for people on stage as they have to largely rely on their imagination to understand how the digitally created characters will behave in the scene. An interesting research direction pertain how to use XR to support mocap actors’ job. Another research direction regards the impact of innovative cinematic techniques on the viewers' experience. For instance, a key step for immersive movies would be the selection of ideal points of view that would make the viewer have the best experience while watching them.

In this context, several thesis proposals are available. For instance, one of the proposals could concern the improvements of an AR system designed to support mocap actors while rehearsing scenes containing visual effects. Another proposal could focus on cinematic AR/VR, and envisage in-depth analyses aimed at supporting the creation of immersive content best matching the viewers’ expectations.

Suggested readings:

[1] Gödde M, Gabler F, Siegmund D, Braun A. Cinematic narration in VR—rethinking film conventions for 360 degrees, pp. 184–20, 2018.

[2] Cannavò A, Pratticò F G, Bruno A, Lamberti F. "AR-MoCap: Using augmented reality to support motion capture acting". In: 30th IEEE Conference on Virtual Reality and 3D User Interfaces (IEEE VR 2023), 2023.

[3] Cannavò A, Castiello A, Pratticò F G, Mazali T, Lamberti F. "Immersive Movies: The Effect of Point of View on Narrative Engagement". AI & Society, 2023.

Extended Reality (XR) technologies, encompassing Virtual (VR), Augmented (AR) and Mixed (MR) Reality, are disrupting the way films are produced and viewed. Although these technologies are getting commonplace, they are presenting new, unexplored challenges to both the actors and final viewers. For instance, when motion capture (mocap) is used to record actors’ movements with the aim of animating digital ...

Several thesis proposals are available regarding the use of haptic feedback in Virtual Reality, Augmented Reality and Mixed Reality experiences. The proposals are in collaboration with different teams of the IRISA-Inria center at Rennes university and include an internship at their premises (http://www.irisa.fr/en/scientific-departments-irisa). Duration of the thesis/internship is approximately 5-6 months. Experience in C/C++/C#, Unity3D, VR/AR tools, human-machine interaction are required. Details are provided below at at the following link:
inria-irisa-rennes-haptics-thesis-internship-april2023.pdf


HapticMic: Studying the Persuasive Effects of Haptics during Speech-Based Interactions in Virtual Reality

Vibrotactile feedback is directly related to sound. When we speak, we make our body vibrate. At a concert, we feel vibratory feedback if we get close to a speaker. Vibrations seem to be an interesting way to emphasize sound feedback. Persuasive speech is broadly used in verbal communication, for instance in meetings, advertisements, informal discussions among friends, etc. Being able to modulate speaker leadership or persuasion in a collaborative environment could be of interest to increase inclusivity (e.g. of shy participants) or to solve conflicts. Previous studies have suggested that leadership is influenced by visual feedback. Very recently, we conducted two experiments where participants embody a first-person avatar attending a virtual meeting in immersive VR. Results showed that vibrotactile-reinforced speech can significantly improve the perceived co-presence but also the persuasiveness and leadership of the haptically-augmented agent. The objective of this research work is to design and evaluate a haptic-enabled microphone, able to reinforce speech in real-time through vibrotactile feedback.


Wearable haptics for augmenting tangible objects in Augmented Reality

Augmented Reality (AR) integrates virtual content into our real-world surroundings, giving the illusion of one unique environment. Virtual object manipulation is critical for useful and effective AR usage, such as in medical applications, training, or entertainment. However, there still exist several issues that affect manipulation in AR, degrading the overall user experience. One of the most important limitation is the lack of haptic feedback, despite being proven to significantly improve both the manipulation performance and the user's experience during manipulation in VR and AR. The lack of haptic sensations is due to multiple reasons, including the degradation of the tracking performance when using, e.g., wearable haptic interfaces, and the resulting mismatch between the pose of the user's hand and that of its avatar representation in the AR environment. The objective of this research work is to design and evaluate (wearable) haptic approaches to augment tangible objects during AR manipulation, e.g., alter the haptic perception of a tangible object through additional wearable haptic stimuli.


Mixed Reality (MR) for heterogeneous multi-robot systems

Teams of coordinated robots have been successfully used in a plethora of different applications, including disaster response, exploration, patrolling, and surveillance. Multi-robot systems are able to perform actions according to the perception of the single robot and understanding of the environment, as the team (physically) interacts with it. The ensemble of these abilities of sensing, interpreting, modeling, predicting, and interacting with the physical world are concrete applications for Artificial Intelligence (AI) tools and methodologies. However, when multiple robots are involved, it is unclear how to provide spatially accurate and efficient information to a (leader) human user, who either control the team from a remote (and secure) location or moves in the same environment as the robots. In this respect, Mixed and Augmented reality can be used to provide the human user with additional information about the target environment and task, reconstructing (part of) it from the information provided by the robotic team. By enhancing the world with virtual information, the user is allowed to provide additional feedback to the robots, that can elaborate those information using sensors and cameras in a complete agnostic way with respect to the environment they move in. The objective of this work is to design and deploy a mixed-reality system able to agnostically treat real and virtual robots, as well as provide feedback information to the user about the status of the robots. Imagine a human user, moving in the same environment of a team of coordinated drones; he or she can see directly some of the drones, while those who are not visible are shown through AR rendering. Moreover, virtual content can be added to the real world to show additional relevant details, such as the location of targets.


Immersive Virtual Reality (VR) multi-sensory User Interface (UI) for controlling multi-robot systems at the microscale

Information feedback during the teleoperation of microrobotic systems is of course fundamental, enabling the human user to understand what is happening in the remote environment. An example of visual feedback commonly provided during the teleoperation of such systems is shown in Figure 1: two microrobots are controlled in 3D, providing the user with the front and side views of the environment as captured by the cameras. Input information regarding, e.g., where the robot should move is usually provided by simply clicking on the screen to indicate the target reference positions. While this approach is widely employed, and it provides all the necessary information to drive the robots, it does not enable an intuitive control and visualization of the robots and their environment. The objective of this research work is to design and evaluate a series of immersive Virtual Reality (VR) User Interfaces (UI) for controlling multi-robot systems at the microscale in an intuitive and natural way, as well as providing feedback information to the user about the status of the robots and the current task.

Several thesis proposals are available regarding the use of haptic feedback in Virtual Reality, Augmented Reality and Mixed Reality experiences. The proposals are in collaboration with different teams of the IRISA-Inria center at Rennes university and include an internship at their premises (http://www.irisa.fr/en/scientific-departments-irisa). Duration of the thesis/internship is approximately 5-6 months. ...

Several new thesis proposals are available, which will be developed in collaboration with HyperTennis (https://hypertennis.it/) a startup / spin-off of Politecnico di Torino. HyperTennis is designing solutions for the application of Artificial Intelligence and other technologies to analyze and improve the performance of tennis players. Thesis works will require the application of Computer Vision and Deep Learning techniques to:
1) perform high-speed, Human Pose Estimation and extract the articulated 3D model of the tennis players from the video feed coming from multiple, synchronized cameras
2) identify relevant tennis players' actions in the execution of technical gestures, i.e. shots (serve, forehand, backhand, etc.)
3) extract accurate information about the ball (trajectories and impact with the racket and the field). 4) automatically classify shot type
5) identify/separate specific phases in the execution of a shot
6) predicting next shot based on historical data

Several new thesis proposals are available, which will be developed in collaboration with HyperTennis (https://hypertennis.it/) a startup / spin-off of Politecnico di Torino. HyperTennis is designing solutions for the application of Artificial Intelligence and other technologies to analyze and improve the performance of tennis players. Thesis works will require the application of Computer Vision and Deep ...

The aim of this thesis, to be developed in collaboration with the Egyptian Museum in Turin, is to develop an algorithm able to reconstruct in a digital way the fragments of archeological remains by analyzing the fracture surfaces of the single pieces. The algorithm could be first tested on a “modern” object reduced to pieces, and then applied to a use case to be agreed with the Museum (e.g., stone fragments from a statuary group, decorated floor or ceiling pieces, ceramic parts, etc.). The match between two fragments shall be identified based on the analysis of the geometries, not of the possible color information that could still be visible on the surface. The algorithm should provide a confidence score for the various matches identified. 3D geometries will be provided by the Museum. These geometries will be created using the Artec Space Spider, a structured light scanner capable to produce meshes with a resolution up to 0.1 mm.

Suggested readings:
https://www.sciencedirect.com/science/article/abs/pii/S1296207422000826
https://ieeexplore.ieee.org/document/4697243
https://www.researchgate.net/publication/333161346_Reassembling_Fractured_Objects_by_Geometric_Matching
https://www.academia.edu/51368373/Automated_reconstruction_of_fragmented_objects_using_their_3D_representation_Application_to_important_archaeological_finds
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3231500/

The aim of this thesis, to be developed in collaboration with the Egyptian Museum in Turin, is to develop an algorithm able to reconstruct in a digital way the fragments of archeological remains by analyzing the fracture surfaces of the single pieces. The algorithm could be first tested on a “modern” object reduced to pieces, and then applied to a use case to be agreed with the Museum (e.g., stone fragments ...

One of the challenges for future intelligent vehicle applications, will be to determine whether a person is authorized to operate the vehicle or not. The face is the most common identifier in humans. Face recognition is a biometric application to recognize and identify the facial patterns of the human faces. These facial patterns of persons are compared and extracted by their facial contours. Anyway, extraneous imaging components, for example, posture, light and outward appearance still reason much inconvenience in exact face acknowledgment.

Furthermore, if the problem of recognizing the owner of a vehicle moves outside the car itself, that is, the system must recognize the owner in exceptional access conditions, the problem becomes even more tough - recognition in the “wild”. It means to build a fast and accurate system that can detect, recognize, and verify the driver’s identity in presence of constraints introduced in the car environment in different lighting and other external conditions. Recently, human pose estimation is an important and widely concerned research topic in computer vision. Given an image or video input, 3D human pose estimation aims to predict the configuration of the human body. The use of body posture can contribute to biometric identification with a multimodal approach.

The focus of this thesis, developed in collaboration with Centro Ricerche Fiat (CRF) and Stellantis, will be on multimodal AI models, combining face identification with body pose estimation to tackle the above challenges.

One of the challenges for future intelligent vehicle applications, will be to determine whether a person is authorized to operate the vehicle or not. The face is the most common identifier in humans. Face recognition is a biometric application to recognize and identify the facial patterns of the human faces. These facial patterns of persons are compared and extracted by their facial contours. Anyway, extraneous ...

Current social media generate a tremendous amount of visual material, that can be exploited by researchers operating in social media research, digital humanities, and marketing. However, privacy regulations impose significant restrictions to both data collection and sharing. The CAMOUFLAGE project, funded by the AI4Media European project, aims to exploit diffusion model (such as Stable Diffusion) to produce a synthetic version of an existing image, presenting equivalent visual and semantic characteristics of the original, while at the same time fully preserving the anonymity of the user who published the image. The project has a strong multidisciplinary focus and will be carried out in collaboration with researchers in visual semiotics to determine the quality and information content of the generated image. The system will be tested on a collection of real Facebook and Instagram profile pictures. Multiple thesis are available tackling three distinct, yet related, research objectives: to design and implement controllable image synthesis that retains the visual and semantic content of a target image; to determine whether the resulting synthetic images can be considered successfully anonymized, in comparison with state-of-the-art solutions based on GANs; and whether the synthetic collection has the same information content of the original image.

Current social media generate a tremendous amount of visual material, that can be exploited by researchers operating in social media research, digital humanities, and marketing. However, privacy regulations impose significant restrictions to both data collection and sharing. The CAMOUFLAGE project, funded by the AI4Media European project, aims to exploit diffusion model (such as Stable Diffusion) to produce a ...

Two thesis proposals in collaboration with Reply Spa are available in the context of self-supervised learning (SSL) and computer vision applied to mobile robots. In particular, the aim of the first thesis work is to develop new methods of SSL in computer vision in order to minimize the bottlenecks and problems of the classical methods of supervised and unsupervised learning. The goal is to adopt and improve an SSL model and then insert it into a real use case in industry or customer.

References:
Masked Siamese Networks for Label-Efficient Learning
https://arxiv.org/abs/2204.07141
SSL method DINO
https://github.com/facebookresearch /dino


The objective of the second thesis work is to apply computer vision models to robotic agents such as Spot, by Boston Dynamics, or parcel delivery robots and other IOT devices. The difficulty of this work lies in being able to compress the power of the SSL models into lighter models capable of giving excellent results having scarce reserves both hardware and connectivity.

References:
Tensorflow lite
https://www.tensorflow.org/lite
Robot Operating System
https://www.ros.org/

Two thesis proposals in collaboration with Reply Spa are available in the context of self-supervised learning (SSL) and computer vision applied to mobile robots. In particular, the aim of the first thesis work is to develop new methods of SSL in computer vision in order to minimize the bottlenecks and problems of the classical methods of supervised and unsupervised learning. The goal is to adopt and improve ...

Training deep neural networks require large scale annotated datasets which are often difficult to collect in the medical domain. As a consequence, a plethora of small and very specialized datasets have emerged. The medical domain would extremely benefit from more general purpose models that can be applied to a variety of problems. A possible strategy to overcome this issue is to leverage self-supervised techniques in which the network is pre-trained by using a pretext task. A simple example is rotation: the image is rotated and the network learns to detect the orientation; more generally, a pretext task can be any for which the labels are automatically generated. If the pre-training dataset is sufficiently large and varied, the features learnt should be sufficiently general to be useful for many downstream tasks. The network is then fine-tuned towards the final task (e.g., lesion detection or segmentation). The goal of this thesis, starting from an existing dataset generated in a previous work, is to systematically explore different self-supervised learning techniques, including generative components, and explore their relationship with the diagnostic/classification tasks. Secondly, we will explore ways to make the features learnt generalize better to different imaging modalities. Strong programming and analytical skills are required, as well as a solid background in machine learning/deep learning.

Suggested readings:
https://arxiv.org/abs/1908.06912
https://arxiv.org/abs/1912.01991

Training deep neural networks require large scale annotated datasets which are often difficult to collect in the medical domain. As a consequence, a plethora of small and very specialized datasets have emerged. The medical domain would extremely benefit from more general purpose models that can be applied to a variety of problems. A possible strategy to overcome this issue is to leverage self-supervised ...

The driving style is unique to each person and it affects the vehicle usage. The thesis aims to create an autonomous agent able to mimic the behavior of a specific driver, in order to perform adaptive DSE (Drive Style Estimation). This requires the agent to be able both to react properly to environmental stimuli and to tailor its behavior on the driver’s profile. Activities could encompass the use of the both datasets collected on real vehicles, as well as data gathered in simulated environments.

The thesis will be carried out in collaboration with Sensor Reply.

The student will be involved in: - Vehicle data analysis and data preprocessing;
- Development of an AI-powered autonomous agent;
- Adapt the agent to the specific driver.


Relevant technologies: Deep Learning, Adversarial Learning, Reinforcement Learning, Autonomous Agent, Edge AI, Cloud Computing, Connected Vehicle

The driving style is unique to each person and it affects the vehicle usage. The thesis aims to create an autonomous agent able to mimic the behavior of a specific driver, in order to perform adaptive DSE (Drive Style Estimation). This requires the agent to be able both to react properly to environmental stimuli and to tailor its behavior on the driver’s profile. Activities could encompass the use of the both ...

The objective of this thesis is to investigate the integration of InSAR (Interferometric Synthetic Aperture Radar) and GNSS (Global Navigation Satellite System) data for landslide monitoring by exploiting deep learning models. Multiple thesis are available in the context of a project funded by NODES (Nord Ovest Digitale e Sostenibile). The thesis will in cooperation with the GRAINS (Prof. Lia Morra) and VANDAL groups (Prof. Tatiana Tommasi), with AIZOON Technology Consulting and with Geomatics Research & Development srl. InSAR is a remote sensing modality for the analysis of radar images captured by satellites equipped with Synthetic Aperture Radar. The radar antenna continuously transmits microwaves toward the Earth’s surface and captures the waves reflected back to the antenna position. By measuring both the amplitude and phase of the backscattered electromagnetic waves it is possible to assess the magnitude and location of ground movements. On the other hand, GNSS receivers on the ground collect information from a constellation of satellites to determine location. By exploiting ad-hoc engineered stations, it is possible to monitor small ground displacements and deformations at selected points. While InSAR provides information over a large area with spatial continuity but temporal discontinuity based on satellite overpasses, GNSS offers measurements with temporal continuity but only at limited spatial positions. The combination of these cues can be formalized as a weakly supervised semantic segmentation problem, where GNSS information is interpreted as sparse ground truth annotations for InSAR data. Following an initial phase dedicated to literature review and data organization, the main focus of this thesis will be developing a deep learning model that given an InSAR map and a few annotated GNSS points, will produce as output complete map annotations. Prerequisites: The successful candidate should be strongly motivated and is expected to have good knowledge of Python programming and PyTorch deep learning framework.

The objective of this thesis is to investigate the integration of InSAR (Interferometric Synthetic Aperture Radar) and GNSS (Global Navigation Satellite System) data for landslide monitoring by exploiting deep learning models. Multiple thesis are available in the context of a project funded by NODES (Nord Ovest Digitale e Sostenibile). The thesis will in cooperation with the GRAINS (Prof. Lia Morra) and ...

While current generative text-to-image latent diffusion models have reached unprecedented results in terms of visual fidelity, there are still open issues to be addressed in exerting precise control over the generated images. Generative models have difficulty in creating correct images when the textual prompt contains many details and often with object placement and spatial awareness. Recent text-to-image latent diffusion models have shown substantial improvements in prompt following, yet still struggle with the use of words such as “left” or “behind”. One possible reason lies in the inherent limitations of the text embedding employed to condition the generation process, that fails to learn sufficiently detailed and disentangled representation. This research project aims to overcome limitations in current generative text-to-image models by exploiting more structured representations for conditioning the image generation process. After an analysis of the state of the art on text-to-image synthesis, the candidate will: i) define a benchmark by indentifying a suitable dataset of challenging prompts, biased outputs and failures will be created by extensively reviewing open and closed source system, as well as the relevant literature; ii) investigate new architectures that richer, more structured representations, such as scene graphs, as an intermediate step to disambiguate textual prompts, incorporate greater spatial awareness and increase control in image composition. Skills possessed or to be acquired: Generative models, latent diffusion models; strong analytical skills and general interest in research-oriented projects.

While current generative text-to-image latent diffusion models have reached unprecedented results in terms of visual fidelity, there are still open issues to be addressed in exerting precise control over the generated images. Generative models have difficulty in creating correct images when the textual prompt contains many details and often with object placement and spatial awareness. Recent text-to-image ...

Context: Social media platforms have a profound impact on the way individuals choose to (re)present themselves in the digital era. Through the analysis of visual big data, we seek to understand how face representations have changed over time. Within the context of the FACETS (Face Aesthetics in Contemporary E-Technological Societies) project, we have collected user profile images from Facebook and Instagram. We are also developing, in collaboration with the Department of Philosophy at Università di Torino, visual big data analytics tools through computational image analysis and deep learning techniques that borrow from other disciplines, such as socio-semiotics and visual semiotics. A computational pipeline, FRESCO, has already been developed to extract semantic characteristics (composition, content, etc.) from images.

Open research questions to be tackled in one or more research thesis include:
- Design and extend the existing computational pipeline with new tools and techniques to extract semantic characteristics from images (composition, content, etc.)
- Develop a user-friendly analytics pipeline to apply the FRESCO pipeline to image collections to extract information about culturally relevant aspects (e.g., how does gender affect the kind of images we publish on social media? how do self-representation change over time?) and for applications in specific domains (e.g., marketing)
- Adapt the proposed pipeline to the analysis of other types of images (advertisement, meme, artworks, AI-generated images, …). Particularly relevant is the use of the proposed pipeline to detect and quantify biases in AI-generated images
- Design, train and evaluate deep neural networks that mimic high-level semantic analysis (which emotions are solicited by a given image? Which values are expressed? )
- Investigate the applicability of Multi-modal Large Language Model (LLM) such as Gemini to extract high-level interpretation from images and reproduce the type of in-depth analysis performed by experts, such as semioticians

Prerequisites: good knowledge of big data analytics, computer vision and deep learning frameworks, and a willingness to engage in multi-disciplinary research

Context: Social media platforms have a profound impact on the way individuals choose to (re)present themselves in the digital era. Through the analysis of visual big data, we seek to understand how face representations have changed over time. Within the context of the FACETS (Face Aesthetics in Contemporary E-Technological Societies) project, we have collected user profile images from Facebook and Instagram. We ...

Image interpretation tasks, such as object detection and visual relationship detection, may benefit from neural-symbolic integration in order to inject prior knowledge, e.g., from knowledge graphs, about objects and their relationship into the training objectives of deep neural networks. The goal of this project is to design deep architectures combining convolutional neural networks and trasformers with neural-symbolic components such as Logic Tensor Networks to solve various image interpretation tasks, such as object detection, image classification or visual relationship detection. Multiple thesis are available to tackle the following open issues: i) how to integrate the knowledge into the training process of high-level semantic interpretation (scene graph generation); ii) how to automatically extract and compile prior knowledge from existing sources and format it for appropriate ingestion; iii) how to use Large Language Models (ChatGPT) to extract prior knowledge and logical constraints; iv) how to characterize neural-symbolic architectures, e.g. in terms of types of features learnt, efficiency and interpretability; v) how to combine concept extraction with knowledge induction to close the neuro-symbolic cycle, and how to simplify knowledge acquisition through human-in-the-loop appraoches

Prerequisites: programming skills (Python, Pytorch or other deep learning framework); good analytical and mathematical skills. Prior knowledge of neuro-symbolic techniques is not required – essential material to study on the topic will be provided. The candidate we are looking for is highly motivated and interested towards research-oriented activities.

Image interpretation tasks, such as object detection and visual relationship detection, may benefit from neural-symbolic integration in order to inject prior knowledge, e.g., from knowledge graphs, about objects and their relationship into the training objectives of deep neural networks. The goal of this project is to design deep architectures combining convolutional neural networks and trasformers with ...

Deep neural networks have shown remarkable performance in the interpretation of medical images. However, they require large datasets for training, and performance may degrade when training on imbalanced datasets. Doctors are typically trained through examples, as well as through structured taxonomies that present prototypical examples of visual features associated with benign and malignant lesions. The goal of the thesis is to design neuro-symbolic architectures, such as Logic Tensor Networks, that can incorporate such constraints during training. Experiments are foreseen on mammography and possibly other medical imaging modalities. Thesis activities include design and comparison of different neuro-symbolic techniques, starting from patch-level classification and moving on towards image-level analysis. Issues to be tackled include how to align visual features computed by the network to human-interpretable concepts and how to encode the constraints in the loss. Evaluation will be performed in terms of performance, robustness, explainability and alignment with human asssessment. Prerequisites: programming skills (Python, Pytorch or other deep learning framework); good analytical and mathematical skills. Prior knowledge of neuro-symbolic techniques is not required – essential material to study on the topic will be provided. The candidate we are looking for is highly motivated and interested towards research-oriented activities.

Deep neural networks have shown remarkable performance in the interpretation of medical images. However, they require large datasets for training, and performance may degrade when training on imbalanced datasets. Doctors are typically trained through examples, as well as through structured taxonomies that present prototypical examples of visual features associated with benign and malignant lesions. The goal of ...

While state-of-the-art deep learning typically uses distributed representations, reasoning is normally useful at a higher level of abstraction. As a result, attempts at combining symbolic AI and neural computation into neural-symbolic systems have been on the increase. Neural-symbolic learning and reasoning is an area of research that aims at integrating relational/symbolic reasoning and knowledge representation (e.g., first order logic language for knowledge representation) by embedding their constructs in deep neural networks. NeSy techniques can be used to solve tasks such as Visual Sudoku Puzzle classification, whose goal is to automatically check the validity of handwritten solutions for Sudoku. The system must learn to distinguish digit by satisfying the rules of Sudoku, without providing any digit label. However, NeSy techniques like LTNs introduce many additional design choices, such as: How to define axiomatic prior knowledge (“how to encode the rules of Sudoku”?); How to ground predicates and connectives? How to choose between different frameworks? Comparison about existing frameworks is limited in literature, and issues in reporting, hyper-parameter selections, etc. affect this comparison. The goal of this thesis is to experimentally and extensively investigate the effect of different design choices on the task of visual Sudoku puzzle classification, from the formulation of the constraints to the hyper-parameter settings, on Logic Tensor Networks designed for this task. In addition, LTNs will be compared against other neuro-symbolic frameworks (such as NeuPSL) and standard deep learning baselines (such as visual transformers and relational networks).

Reading list:
Morra, Lia, et al. "Designing Logic Tensor Networks for Visual Sudoku puzzle classification." 17th International Workshop on Neural-Symbolic Learning and Reasoning (NeSy 2023). CEUR, 2023.
https://www.cs.ox.ac.uk/isg/conferences/tmp-proceedings/NeSy2023/paper19.pdf

Prerequisites: programming skills (Python, Pytorch or other deep learning framework); good analytical and mathematical skills. Prior knowledge of neuro-symbolic techniques is not required – essential material to study on the topic will be provided, as well as an initial implementation. The candidate we are looking for is highly motivated and interested towards research-oriented activities.

While state-of-the-art deep learning typically uses distributed representations, reasoning is normally useful at a higher level of abstraction. As a result, attempts at combining symbolic AI and neural computation into neural-symbolic systems have been on the increase. Neural-symbolic learning and reasoning is an area of research that aims at integrating relational/symbolic reasoning and knowledge ...

Artificial Intelligence agents are required to learn from their surroundings and to reason about the knowledge that has been learned in order to make decisions. While state-of-the-art deep learning typically uses distributed representations, reasoning is normally useful at a higher level of abstraction. As a result, attempts at combining symbolic AI and neural computation into neural-symbolic systems have been on the increase. Neural-symbolic learning and reasoning is an area of research that aims at integrating relational/symbolic reasoning and knowledge representation (e.g., first order logic language for knowledge representation) by embedding their constructs in deep neural networks. The goal of this project is to automate the process of designing, implementing and setting the hyper-parameters of neuro-symbolic systems. In fact, NeSy techniques like LTNs introduce many additional design choices and hyper-parameters, such as: i) how to define constraints, which to include, in which order and how to weight them; ii) how to redefine the neuro-symbolic loss, which functions to use and how to parameterize them; iii) how to deal with common dataset issues such as class imbalance and dataset bias; iv) how to prevent NeSy losses to promote unwanted behaviors, such as reasoning shortcuts.

Several thesis are available on tackling these issues. Depending on the candidate background and predisposition, the problem can be tackled either from a more theoretical or experimental standpoint. Starting from an analysis of the current literature, techniques to be investigated include: i) Analysis of the mathematical and numerical properties of different frameworks; ii) Comparison with other frameworks and losses; iii) application of techniques from the fields of hyper-parameter optimization (Random search, Bayesian Optimization), Neural Architectural Search (NAS), genetic algorithms and curriculum learning. Experiments will be conducted on standard benchmarks as well as applications on semantic image interpretation explored by the group.

Prerequisites: programming skills (Python, Pytorch or other deep learning framework); good analytical skills. Prior knowledge of neuro-symbolic techniques is not required – essential material to study on the topic will be provided. The candidate we are looking for is highly motivated and interested towards research-oriented activities.

Artificial Intelligence agents are required to learn from their surroundings and to reason about the knowledge that has been learned in order to make decisions. While state-of-the-art deep learning typically uses distributed representations, reasoning is normally useful at a higher level of abstraction. As a result, attempts at combining symbolic AI and neural computation into neural-symbolic systems have been ...

The thesis aims at designing and developing a tool for the analysis of smart contracts deployed on existing blockchain platforms. The objective of the tool is to support the user in the identification of smart contracts' main characteristics (e.g., in terms of application field, type, tasks performed, interactions with other smart contracts, etc.), by also exploiting visualization techniques. The thesis student will also investigate the added value of search engines for smart contracts, and will enrich the developed tool with an "intelligent" search feature, e.g., based on semantic technologies.

The thesis aims at designing and developing a tool for the analysis of smart contracts deployed on existing blockchain platforms. The objective of the tool is to support the user in the identification of smart contracts' main characteristics (e.g., in terms of application field, type, tasks performed, interactions with other smart contracts, etc.), by also exploiting visualization techniques. The thesis student ...

The adoption of electric vehicles (EVs) is increasing, and the demand for electric vehicle recharging infrastructure is growing. To ensure the efficient operation of this infrastructure, new tools are needed to simulate, test, and optimize different charging strategies. In this thesis proposal, we propose the design and implementation of a blockchain-based simulation tool for electric vehicle recharging infrastructure that can:
- provide an immutable and decentralized record of all charging transactions and other relevant data;
- exploit different consensus mechanism to model, simulate, and test different charging strategies for electric vehicle recharging infrastructure.

In the first part of the thesis the student will be asked to develop a blockchain oracle represented by the porting in Python of an existing MATLAB model.

In the second part, the student will design and develop the blockchain platform based on an Ethereum-like (e.g., Quorum, Polygon) or an Hyperledger (e.g., Fabric, Besu) blockchain.

In the final phase the oracle will be connected to the blockchain platform to test and validate the integrated simulation tool.

The methodology for this thesis will include both theoretical and practical research. The theoretical research will involve a thorough analysis of existing blockchain consensus and their limitations, as well as a study of dynamic models and external oracles. The practical research will involve the design, implementation and testing of the simulation tool composed by the external oracle and the blockchain platform, as well as the evaluation of its performance and security. The thesis will be developed in collaboration with LINKS Foundation.

The adoption of electric vehicles (EVs) is increasing, and the demand for electric vehicle recharging infrastructure is growing. To ensure the efficient operation of this infrastructure, new tools are needed to simulate, test, and optimize different charging strategies. In this thesis proposal, we propose the design and implementation of a blockchain-based simulation tool for electric vehicle recharging ...

Self-Sovereign Identity is a new digital identity model that empowers individuals to control their identity without relying on centralized authorities. SSI enables the creation, management, and verification of digital identities that are self-owned, portable, and interoperable. In the context of an energy data marketplace, SSI can potentially enable secure and efficient sharing of energy data while giving individuals control over their data.

This thesis aims to explore the concept of Self-Sovereign Identity (SSI) and its potential integration within an energy data marketplace. The purpose of the research is to analyze the algorithms used to enable the methodology of SSI, and investigate the implications of adopting SSI in an energy data marketplace. This research aims to contribute to the body of knowledge on the use of SSI in various applications and provide a practical demonstration of its implementation in an energy data marketplace.

In particular, the research will begin with a comprehensive review of the literature on SSI and its algorithms and will examine its applicability to an energy data marketplace. The study will investigate the technical and social implications of SSI integration in the marketplace and identify potential benefits and limitations.

The research will then proceed to identify the most suitable SSI algorithms for the energy data marketplace and investigate the feasibility of implementing them.

The thesis will be developed in collaboration with LINKS Foundation.

Self-Sovereign Identity is a new digital identity model that empowers individuals to control their identity without relying on centralized authorities. SSI enables the creation, management, and verification of digital identities that are self-owned, portable, and interoperable. In the context of an energy data marketplace, SSI can potentially enable secure and efficient sharing of energy data while giving ...

Industrial product tracking is a critical aspect of supply chain management that ensures the integrity, traceability, and security of products as they move through the supply chain. Blockchain technology has the potential to revolutionize industrial product tracking by providing a tamper-proof and decentralized system for data storage and traceability. However, with various blockchain platforms available, it is important to evaluate the suitability of each platform for industrial product tracking based on both technical and business aspects.

This thesis aims to conduct a benchmarking of blockchain platforms for the traceability of a specific industrial prototyping process using a SWAT (Strengths, Weaknesses, Opportunities, Threats) analysis considering relevant technical and business aspects (e.g., as cost alignment, sustainability, scalability, longevity, integration, regulatory compliance, and access to blockchain development skill) and demonstrating the advantages of a blockchain-based solution for industrial production traceability through the development of a proof-of-concept.

The specific objectives if the thesis work include:
- Research and analyze existing blockchain platforms for industrial product tracking
- Conduct a SWAT analysis of the blockchain platforms considering relevant technical and business aspects
- Evaluate the suitability of each blockchain platform for industrial product tracking based on the results of the SWAT analysis
- Provide recommendations for the most suitable blockchain platform for industrial product tracking based on the findings.
- Implement a proof-of-concept using one of the selected platform to demonstrate the effectiveness of a blockchain-based approach to for industrial production traceability.

The expected results of this thesis include a comprehensive benchmarking of blockchain platforms that will provide insight into the suitability of each blockchain platform for industrial product tracking and recommendations for the most suitable blockchain platform based on the findings, together with an actual implementation of a blockchain-based solution to demonstrate the feasibility of the proposed solution.

The thesis will be developed in collaboration with LINKS Foundation.

Industrial product tracking is a critical aspect of supply chain management that ensures the integrity, traceability, and security of products as they move through the supply chain. Blockchain technology has the potential to revolutionize industrial product tracking by providing a tamper-proof and decentralized system for data storage and traceability. However, with various blockchain platforms available, it is ...