Projects

This project studies new dependently typed systems suitable for the development and mechanization of programming language semantics. Particular emphasis is given to languages supporting concurrency and non-determinism, such as transition systems and process calculi. Popular proof assistants based on dependent type theory, such as Agda and Coq, are inadequate for the formal verification of the denotational semantics of such languages because of their insufficiently expressive type systems. We address this issue by extending modern type-theoretic frameworks, such as homotopy type theory, with a new class of coinductive types coming from final coalgebras of accessible functors. In denotational semantics, these types are necessary for handling the non-deterministic and continuously-interactive behavior of processes. The resulting more expressive type systems will prove themselves capable of encoding the formal semantics of various languages with concurrency and non-determinism.

A platform for monitoring energy usage and operational state properties of distribution networks is elaborated, enabling users of distribution networks to access relevant and better time-resolution data to support green energy usage, energy efficiency and the flexibility technologies. The platform being developed will provide extensive support for decision-making and calculation algorithms, enabling efficient availability and management of local area operational data.

A cloud-based digital health platform for private clinics in East Africa will be developed, focusing on optimizing workflows, monitoring chronic patients, and managing patient care. A distinctive feature of the platform is the combination of a mobile application and an SMS-based triage solution, allowing access for patients with limited digital capabilities. The product is being piloted in Kenya, where a shortage of doctors and a growing population create strong demand for efficient, labor-saving solutions. The solution is based on the logic of primary care digital models tested in Estonia, adapted to the local context.

The project will develop a smart, adaptive electric drive that increases the energy efficiency and reliability of electric vehicles. The innovative solution combines artificial intelligence-based control with advanced sensor technology, allowing the drive to adapt in real-time to changes in traffic and road conditions. The project will produce a laboratory prototype, a user-friendly software solution for data processing, and comprehensive documentation that will simplify the implementation of the system.

Fish-AI is a real-time automatic video-based fish monitoring system developed by TalTech that automates reporting in accordance with EU requirements and significantly reduces labor costs. The system is compatible with various underwater cameras, detects different water conditions, and is able to adapt to them.

The project will develop a computer simulation model that will enable the assessment of fish passage conditions through hydroelectric power plant turbines. The use of simulations will significantly reduce the need for field tests, allowing for more efficient use of time and money and a broader analysis of scenarios.

The goal of the project is to develop and integrate a personal student assistant based on large language models (e.g. GPT) into the learning environment of Tallinn University of Technology (TalTech). The assistant will be connected to the university's Moodle learning environment and will be able to provide fast and personalized feedback, helping students with course completion and solving assignments.

Platform work is a growing form of employment in Estonia, impacting social security options, career paths, and traditional forms of employment. It is essential to understand the economic and social outlook of workers engaged in platform work and the future of this sector in Estonia, in relation to developments in the European Union. This interdisciplinary project, which brings together social scientists, legal scholars, and economists, offers insights into the field of platform work and its broader impacts on the Estonian economy through various quantitative and qualitative methods. The central outcome of the work is a scenario-based analysis of the potential future development of platform entrepreneurship in Estonia, including an examination of emerging risks and opportunities-, considering regulatory developments in Europe, macroeconomic trends, and technological advancements. It also considers the impact of different scenarios on society, including the potential increase in vulnerability. The report produced within the framework of the project, and the related activities carried out within the project are based on the need to understand platform work more broadly while also considering its potential impacts on the development of social security in Estonia.

Visible effects of society’s ageing is the reported increase in the necessity of orthopaedic implants boosting to a huge economical market. For a successful integration of any implant, bone regeneration, osseointegration at the interface bone and implant as well as mitigating inflammatory events are crucial aspects. The project aims at extending the biocompatibility and tribo-mechanical performance associated with a lifetime of surgical implants based on Titanium (Ti). The hypothesis states combining Ti alloy with hydroxyapatite (HAp), and medically active components (drug delivery function) in order to obtain excellent biomaterial supporting bone growth and eliminating the problem of loosening of the implant by its integration with bone. The project goes beyond the state-of-the-art by laser surface treatment, opening the underlying porosity improving the cell transport and cell growth. The result implant biomaterial, will reduce the number of removal surgeries in the future.

The DEUCE Project aims at better enforcement of judicial decisions (judgments, authentic instruments, and court settlements) issued in the context of cross-border pecuniary claims. It particularly focuses on the creation of a Roadmap of the 26 EU Member

This project focuses on the development and integration of Extended Reality and existing digital tools to support advanced engineering education in manufacturing. The purpose of the XREN is to bring back the results of research activities in the field of digital manufacturing to the engineering students. Since, process modelling, analysis, virtual and augmented reality, as well as the role of the human workers in the factories have been among the main research topics in manufacturing. Therefore, the project aims to experiment, test and validate learning approaches based on XR technologies, focusing the attention on: • Development of a VR environment to support learning activities in the area of manufacturing, namely in the design and analysis of manufacturing systems. • Development of an AR approach to support learning activities in the area of mechanical design and maintenance process. • Develop approaches and methodologies to analyze and evaluate the learning mechanisms based on these technologies.

The aim of this applied research is to improve the usage of radars for autonomous navigation of unmanned land vehicles in off-road scenarios. As radars use radio waves for their operation, they have several advantages over optical sensors such as cameras and lidars. For example, weather phenomena such as snow, rain, fog, etc. have much less impact on the performance of radars than optical sensors. Also, radars are not disturbed by dirt and other small debris, making them an ideal sensor for off-road navigation. However, radars do have one major drawback: the resolution of the point cloud they generate is significantly lower than that of, for example, lidars. This research will explore ways to improve the quality of data from radars using artificial intelligence and other appropriate algorithms.

In the wake of the COVID-19 pandemic and the ensuing effects on society and the economy, there has been a significant increase in concerns about the possibility that malicious actors could return to using hazardous agents in future plots. These concerns are legitimate in Europe, where there are still technological gaps in several aspects of the CBRN Security Cycle and specifically in the devices for rapid detection, identification and monitoring of low-volatile chemical warfare agents (CWAs) and non-volatile biological warfare agents (BWAs), mainly in complex natural environments. Benchmark technologies, including IMS, GC-IMS, and Py-GC-IMS, can sample and identify the most volatile CWAs within seconds (IMS) or BWAs within minutes (Py-GC-IMS), even at low ppbV concentration levels, but cannot detect extremely low doses of low-volatile toxic fourth generation CWAs (e.g. Novichoks), nor can they differentiate biological fragments from harmless substances. To overcome these gaps, it is necessary to develop new highly selective and sensitive detectors with detection limits in the pptV range, operated at elevated temperatures (> 200 °C) to prevent condensation of low volatile constituents, high 2D resolving power and robust analytical methods. TeChBioT aims for the development of a universal detection technology based on high-temperature (HT) ion mobility spectrometry (IMS) with optional gas chromotographic pre-separation (GC) and pyrolysis (Py) for enabling fast detection and identification of nonvolatile biological and low-volatile chemical agents. The innovative technology is combined with Artificial Intelligence (AI) and Deep Learning (DL) models to reduce the dimensionality of the 2D spectral data and enable distinguishing of bacteria, fungi, viruses, low volatile chemical warfare agents, and toxic industrial compounds at pptV concentration levels based on their unique fingerprint within a complex environment.