Tallinn University of Technology

Department of Software Science

Table of Contents

Applied Artificial Intelligence Group

Head of the research group: Development Officer AGO LUBERG, ago.luberg@taltech.ee

The Applied AI Group conducts research in application of AI methods in various fields and systems. We investigate applicability of machine learning, ontology based reasoning, automated theorem provers, knowledge discovery and other AI methods for solving digitalisation problems of different industrial and governmental stakeholders.

Our previous research has been concentrated on building software development methods and tools (e.g. CoCoViLa) with AI components, basically with program synthesis and ontology based knowledge representation components.

During a number of decades several software tools that facilitate AI techniques have been developed by the group. The following is a list of tools that are still in use or under deveopment:

  • CoCoViLa – visual model-based software develpment environment;
  • WhiteDB – a lightweight NoSQL database library;
  • GKC – discussion tool on large knowledgebases.

Currently we work on topics like application of AI methods in spatial data analysis, using machine learning for risk management in e-commerce and for public service delivery. The corresponding projects are listed as follows:

  1. Applied research for creating a cost-effective interchangeable 3D spatial data infrastructure with survey-grade accuracy
  2. Applied research for e-commerce EU VAT and duty declaration (as from 2021) digitalisation
  3. Machine learning and AI powered public service delivery

Projects:

Artificial intelligence (AI) has a strong and growing potential in helping to address challenges in many societal contexts. The capabilities of AI in interpreting complex data and generating solutions have been amplified through the use of foundation models such as large language models. The Estonian Centre of Excellence in Artificial Intelligence (EXAI) focuses on advancing innovative methodologies for (1) leveraging foundation models in building efficient and trustworthy analysis and prediction systems; (2) implementing control mechanisms and guardrails to ensure that the advanced AI systems follow their specification; (3) adapting and enhancing AI systems for improved performance in targeted application contexts; and (4) achieving end-to-end security and privacy assurance of AI systems. We apply these methodologies to advance AI capabilities in key Estonian sectors, including e-governance, healthcare, business process management, and cybersecurity.

Contact: Rain Ottis, Pawel Maria Sobocinski, Tanel Alumäe, Tanel Tammet
Participating research and development institutions: Tartu University, Cybernetica AS, Tallinn University of Technology
Funder: Estonian Ministry of Education and Research
Period: 01.01.2024–31.12.2030
ETIS

Our broader mission is to pioneer connecting research in ‘smartification’ and exclusion. We focus on ’smart rurality’ and the elderly – typically disadvantaged spaces and social group in terms of the newest smart solutions. We will study the individual- and place-level enablers and barriers to smartification and carefully unpack the digital innovation biographies of 4 model localities in Estonia: Rõuge, Hiiumaa, Toila and Paide. With a strong interdisciplinary team, we will gather empirical evidence by triangulating methods such as interviews with seniors and a real-time learning- experiment, supported by invited community researchers, reflection seminars, social hackathons, and expert interviews. We will apply an inclusive and participative approach by placing the elderly involved in the research at the heart of the co-creation process and by intervening carefully – but intentionally – in local innovation dynamics.

Contact: Tanel Tammet
Participating research and development institutions: Tartu University, Tallinna University of Technology
Funder: Estonian Research Council
Period: 01.01.2023–31.12.2027
ETIS

A Digital Product Passport (DPP) is a structured digital collection of product-related information, including data on sustainability and circularity performance. DPPs are an essential tool to support European policies towards the twin, Green and Digital, transitions. Built on the results of the CIRPASS project, CIRPASS-2 will support 13 pilot deployments of DPP-enabled circular use cases, across multiple complex value chains in the textiles, electronics, tires and construction sectors. DPP system interoperability will be demonstrated for all pilots and confirm viable cross-sectoral, large-scale deployment of the DPP in real-life settings. The DPP Information System deployed will be aligned to European harmonized standards and regulatory requirements. The project will assess the information understanding and satisfaction of consumers, end-users and authorities in using the DPP. The environmental, economic and policy impacts of the DPP, as well as its benefits and challenges for all stakeholders, will be assessed.

PI: Rain Eisler
Funder: European Commission
Period: 01.05.2024–30.04.2027
ETIS

Business Information Technology Group

Head of the Group: GUNNAR PIHO, gunnar.piho@taltech.ee

The Business Information Technology Group explores methodologies, tools, models, and best practices related to the development, maintenance, and analysis of business IT solutions. The key focus is on theories of reliable, interoperable and evolving applications and their practical solutions. The group works closely with the eHealth Centre and participates in the research and development of eHealth digital solutions in the framework of eMedLab.

Current research topics include:

  • health informatics

  • educational informatics

  • mobile voting

  • applications of artificial intelligence and machine learning

  • applications of natural language processing (NLP)

The Business Information Technology research group contributes to the education of future specialists by teaching in both bachelor’s and master’s programmes in business IT.

Projects:

The overall goal of the project is to increase the number of healthy life years of the population. Currently, Estonia has recorded one of the lowest number of healthy life years at birth in the EU. To achieve this goal, three closely related areas of digital health are researched, developed and piloted. We use the standardized data exchange environment and digital data of the Estonian health information system (EHIS) to develop applications that increase the use of data collected by the person for health promotion, prevention and control of chronic conditions. Second, we focus on sensors and digital applications supported by artificial intelligence (AI) to allow a person to collect both biosignals and textual data in machine-readable form. With this, we speed up the detection of health risks and reduce the healthcare workload. Thirdly, we develop various AI methods by combining the data in EHIS and the Health Insurance Fund's database, as well as the data collected by the person.

Contact: Gunnar Piho (PI: Peeter Ross)
Funder: Estonian Research Council
Period: 01.01.2024–31.12.2028
ETIS

This multidisciplinary study aims to decrease cardiovascular mortality in Estonia by increasing treatment adherence and empowering patients to create a supportive self-management environment for monitoring their health and actively participating in the treatment process. Analysing 1) the LDL-cholesterol values of North Estonia Medical Centre (NEMC) patients to find underdiagnosed and undertreated patients and 2) treatment adherence to lipid-lowering drugs (LLD). Identifying patient groups who need additional support. During the pilot project, a novel application will be developed, together with personal support, used to increase LLD adherence. The novelty of the tool – combining the data used in Estonia from the Nationwide Health Information System, ePrescription, and NEMC electronic medical record with the data collected by the patient and enabling two-way communication between the patient and medical staff. In the last stage of the study, an impact assessment of the tool is planned.

Contact: Gunnar Piho (PI: Magus Viigimaa)
Funder: Estonian Research Council
Period: 01.01.2024–31.12.2028
ETIS

Centre for Digital Forensics and Cyber Security

Head of the centre: professor RAIN OTTIS, rain.ottis@taltech.ee

TalTech Centre for Digital Forensics and Cyber Security works towards raising Estonian cyber security competence and capacity through education, research and knowledge transfer. The research team includes experts from various scientific disciplines, including computer science, law and psychology. Such a team can take on today’s complex cyber security issues that require an interdisciplinary approach.

The main research directions of the Centre are:

  • critical Information Infrastructure Protection (focus on eGovernance and transportation sectors);
  • cryptography;
  • network monitoring;
  • digital forensics;
  • education research;
  • cyber security strategy and policy.

Projects:

Artificial intelligence (AI) has a strong and growing potential in helping to address challenges in many societal contexts. The capabilities of AI in interpreting complex data and generating solutions have been amplified through the use of foundation models such as large language models. The Estonian Centre of Excellence in Artificial Intelligence (EXAI) focuses on advancing innovative methodologies for (1) leveraging foundation models in building efficient and trustworthy analysis and prediction systems; (2) implementing control mechanisms and guardrails to ensure that the advanced AI systems follow their specification; (3) adapting and enhancing AI systems for improved performance in targeted application contexts; and (4) achieving end-to-end security and privacy assurance of AI systems. We apply these methodologies to advance AI capabilities in key Estonian sectors, including e-governance, healthcare, business process management, and cybersecurity.

Contact: Rain Ottis, Pawel Maria Sobocinski, Tanel Alumäe, Tanel Tammet
Participating research and development institutions: Tartu University, Cybernetica AS, Tallinn University of Technology
Funder: Ministry of Education and Research
Period: 01.01.2024–31.12.2030
ETIS

The European and international maritime industries face serious threats from cybercriminals. Shipping companies and ports have begun falling prey to computer viruses, shipping vessels have become vulnerable to GPS jamming and spoofing attacks. Tallinn University of Technology (TalTech) is part of an Estonian ecosystem of cyber security and maritime actors. Therefore, to unite these capabilities in a focused and integrated manner the ERA Chair in Maritime Cyber Security is formed by an outstanding researcher and research coordinator together with a team of experienced researchers. The established new multidisciplinary Centre for Maritime Cyber Security at TalTech will focus on three research sub-topics:

  • Marine Communication;
  • Human Aspects of Cyber Security; 
  • Cyber Security Aspects of Maritime Technology.

PI: Olaf Manuel Maennel; Dan Heering; Rain Ottis
Funder: Euroopa Komisjon
Period: 01.01.2021–31.12.2025
ETIS

The main goal of the project is to answer the questions of how the EU should act in an increasingly complex and conflicting global situation in order to ensure legality, including which factors should be emphasized and which institutions should be strengthened. The research team researching institutional differences across six policy issues – climate change, digitalisation, finance/taxation, health, migration and security – to determine which institutional combinations enable the EU to exert optimal influence on a specific policy issue. As part of the project, an international team of researchers, explore variation in formality (formal to informal), accessibility (open to closed), and normativity (expressed purpose is technical to openly normative).

Contact: Eneken Tikk
Partners: Norsk Utenrikspolitisk Institutt, Copenhagen Business School, Universite Libre De Bruxelles, Universiteit Maastricht, University Of The Witwatersrand Johannesburg, Universita Commerciale Luigi Bocconi, Stichting Vu, European Council On Foreign Relations (Ecfr) E.V, The Hebrew University Of Jerusalem, University Of Ottawa, Waseda University
Funder: European Commission
Period: 01.03.2023–28.02.2027
ETIS

High-assurance Software Laboratory

Head of Laboratory: Lead Research Scientist TARMO UUSTALU, tarmo.uustalu@taltech.ee

The group conducts research into theories, methods and tools for developing high-assurance software, specializing on both proofs (certified software) and testing.

Research results:

  • A dataset for detection of Android malware was collected and curated that covers the full history of the operating system.
  • A construction was given for a digital signature scheme that makes it possible to use the same key pair multiple times based on timestamping.
  • Focused sequent calculi were identified for partially normal skew monoidal, skew prounital closed ja skew symmetric monoidal closed categories.

Projects:

The present project aims to develop novel and enhance existing methods of explainable artificial intelligence for the analysis of human motor functions. Pilot studies have demonstrated promising results to support the diagnosis of neurodegenerative diseases. In addition, we plan to extend the area of application from medicine to cognitive development and cognitive fatigue analysis. The integration of the explainer component will provide medical professionals with the necessary transparency of the decisions made by AI. Application in the area of cognitive development to support the school education process. Cognitive fatigue is known to cause severe injuries and serious financial losses. In-depth understanding of this phenomenon and ability to recognise mental fatigue targets to make the work environment safer and reduce monetary and non-monetary losses in the process of work.

PI: Sven Nõmm
Funder: Estonian Research Council
Period: 01.01.2024–31.12.2028
ETIS

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.

PI: Niccolò Veltri
Funder: Estonian Research Council
Period: 01.01.2022–31.12.2025
ETIS

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.

PI: Evelin Halling
Funder: The State Shared Service Center
Period: 01.07.2024−30.06.2026
ETIS

Information Systems Group

Head of the research group: professor DIRK DRAHEIM, dirk.draheim@taltech.ee

The Information Systems Group conducts research in large- and ultra-large-scale IT systems. We investigate the architecture, design, realization and management of IT system landscapes, high- volume data-intensive systems, high-volume workflow-intensive systems, massively resource-intensive systems and highly distributed systems. In particular, we investigate the next generation of digital government technologies and digital government ecosystems. Together with our partners from industry, academia and the public sector we strive for excellent solutions for non-standard, mission-critical IT system problems.

Projects:

Laboratory for Compositional Systems and Methods

The laboratory started functioning in 2020

Head of the laboratory: Professor PAWEŁ SOBOCIŃSKI, pawel.sobocinski@taltech.ee
https://www.ioc.ee/~pawel/

The group's goal is to study compositional techniques in the context of models of computation, understood broadly. Compositionality means that syntactic descriptions for (open) systems are designed to be compatible with their semantics. While the examples motivating the research come from a broad section of scientific disciplines (logic, control theory, formal language theory, business processes, game theory, economics, machine learning), we have identified common principles for reasoning about open systems, guided by category theory. These include a semantic universe based on relations rather than functions, and the use of the diagrammatic syntax of string diagrams. String diagrams provide an intuitive calculus for computations via diagrammatic reasoning, and fine-grained control over resources, which is important for faithful descriptions of open systems.

The group's big questions/challenges are:

  • design a next generation of programming/specification languages that will be more suited for compositional (and therefore, more trustworthy and reliable) descriptions of systems;
  • use compositionality to improve the analysis of systems, including the design of new techniques and algorithms;
  • design and implement tools for working with string diagrams, fast-tracking the passage from theory to practice.

Projects:

Artificial intelligence (AI) has a strong and growing potential in helping to address challenges in many societal contexts. The capabilities of AI in interpreting complex data and generating solutions have been amplified through the use of foundation models such as large language models. The Estonian Centre of Excellence in Artificial Intelligence (EXAI) focuses on advancing innovative methodologies for (1) leveraging foundation models in building efficient and trustworthy analysis and prediction systems; (2) implementing control mechanisms and guardrails to ensure that the advanced AI systems follow their specification; (3) adapting and enhancing AI systems for improved performance in targeted application contexts; and (4) achieving end-to-end security and privacy assurance of AI systems. We apply these methodologies to advance AI capabilities in key Estonian sectors, including e-governance, healthcare, business process management, and cybersecurity.

Contact: Rain Ottis, Pawel Maria Sobocinski, Tanel Alumäe, Tanel Tammet
Participating research and development institutions: Tartu University, Cybernetica AS, Tallinn University of Technology
Funder: Ministry of Education and Research
Period: 01.01.2024–31.12.2030
ETIS

The reality of ever more complex interactions of software in our daily life has led to classical theoretical models on which computer science is founded on being insufficient for new applications, and no longer sufficient to explain failures, predict behaviors, and ensure the trustworthiness of software infrastructure. Automata theory is one such foundation. Our project will extend and exploit classical automata theory along three dimensions. First, we will study symbolic automata which are a relatively new direction of research with many promising applications. Second, we will explore how automata can be used to explain the interaction of computer programs with their environments. Third, we will explore how automata can be used to interconnect with each other, in analogy with how computers are increasingly more connected in the world around us. The three dimensions represent diverse subfields of computer science and will interact to result in new theoretical models and new algorithms.

PI: Paweł Maria Sobociński
Funder: Estonian Research Council
Period: 01.01.2021–31.12.2025
ETIS

The proposed Cyber-security Excellence Hub in Estonia and South Moravia (CHESS) will integrate leading cybersecurity institutions and capitalize on the strengths of both regions to address important Europe-wide challenges. South Moravia is a major ICT industry & education powerhouse of the Czech Republic, with a very focused and coherent smart specialization strategy targeting cybersecurity. Estonia is among the most advanced digital societies globally, with exceptional e-government deployment – which, however, makes it vulnerable to various cyber threats. CHESS will directly follow the strategies and roadmaps of the European Cybersecurity Competence Pilots and build on the experience of CHESS partners involved in all four of these pilots, contributing to safe transition of the EU to full-scale digital society. The CHESS Hub will conduct a thorough needs analysis of the two regions and develop a joint cross-border R&I strategy for cybersecurity. The strategy development will be aided by implementation of pilot R&I projects that will reinforce the cross-regional collaboration, engage regional innovation ecosystems and build evidence for future projects. Gaps in skills and expertise identified in the regions will be removed by training and knowledge transfer. Finally, dedicated task forces will ensure sustainability of CHESS by integration with regional, national, and EU-level strategies and funding programmes. To exploit the project outputs, especially the pilot project results, CHESS will aid with market potential assessment and link researchers and innovators with entrepreneurship training and business consultancy services available in the regions. The strategizing, skills-building and pilot R&I will cover the totality of the cybersecurity field, with special attention to 6 Challenge Areas: Internet of Secure Things; Security Certification; Verification of Trustworthy Software; Blockchain; Post-Quantum Cryptography; and Human-centric Aspects of Cybersecurity.

Contact: Paweł Maria Sobociński
Partners: Masarykova Univerzita, Tallinna Tehnikaülikool, Tartu Ülikool​​​​, Vysoke Uceni Technicke V Brne, Cybernetica AS, Red Hat Czech S R O​​​, Guardtime OÜ, Riigi Infosüsteemi Amet, Cybersecurity Hub, National Cyber and Information Security Agency, JIC, Zajmove Sdruzeni Pravnickch Osob, MTÜ Eesti Infoturbe Assotsiatsioon
Funder: European Commission
Period: 01.01.2023–31.12.2026
ETIS

Some great recent mathematical advances were driven by the rise of homotopy-theoretic and higher-categorical ideas in new contexts, extending to formal methods in theoretical computer science. Intriguingly, notions of “directed space” have emerged simultaneously in rewriting, concurrency, and type theory, suggesting a unifying homotopical perspective on computation. We will develop this vision in the theory of diagrammatic sets, a new model of directed spaces that promises to connect and refine others. In one part, we will work on type theories for diagrammatic sets, producing new methods for formalised rewriting and linking it to recent synthetic approaches to homotopy theory. In another, we will explore a model of computation centred on a notion of directed homotopy, as a higher-categorical foray into computability and complexity theory. This interdisciplinary project will form new bridges between mathematics and computer science, promoting a flow of ideas that will benefit both.

PI: Amar Hadzihasanovic
Funder: Estonian Research Council
Period: 01.01.2022–31.12.2025
ETIS

To recent exploit advances in high-level approaches to probabilistic modelling (Markov categories, conditioning, partial Markov categories) and in string diagrammatic approaches to logic (cartesian bicategories for regular logic, discrete cartesian restriction categories) to develop expressive and principled graphical specification languages for probabilistic applications. This project will bring together experts on string diagrammatic logic with experts in Markov categories to work on uniting the two approaches.

PI: Paweł Maria Sobociński
Funder: Advanced Research+Invention Agency
Period: 06.09.2024–31.08.2025
ETIS

The project aims to develop a combinatorial and diagrammatic syntax, as well as categorical semantics for multimodal Petri nets as a specification of dynamical systems that can exhibit mode or phase transitions which modify the vocabulary of possible places, events and interactions.

PI: Amar Hadzihasanovic
Funder: Advanced Research+Invention Agency
Period: 05.09.2024−31.08.2025
ETIS

Laboratory of Language Technology

Head of the laboratory: Senior Reseacher TANEL ALUMÄE, tanel.alumae@taltech.ee

The Language Technology Laboratory focuses on the following topics:

  • speech recognition;
  • speaker, spoken language and accent identification;
  • speech corpora;
  • phonetics (Estonian language prosody and vocal system, L2 speech);
  • various sub-topics of natural language processing.

One of the important activities is the creation of speech technology applications targeted at society as a whole. This includes applications of end-user speech recognition as well as the key integration components that are easy to integrate. Although the focus is on speech recognition in Estonian, most of the software created in the laboratory is not specific to Estonian. The laboratory is a solid open source free software supporter.

Projects:

Artificial intelligence (AI) has a strong and growing potential in helping to address challenges in many societal contexts. The capabilities of AI in interpreting complex data and generating solutions have been amplified through the use of foundation models such as large language models. The Estonian Centre of Excellence in Artificial Intelligence (EXAI) focuses on advancing innovative methodologies for (1) leveraging foundation models in building efficient and trustworthy analysis and prediction systems; (2) implementing control mechanisms and guardrails to ensure that the advanced AI systems follow their specification; (3) adapting and enhancing AI systems for improved performance in targeted application contexts; and (4) achieving end-to-end security and privacy assurance of AI systems. We apply these methodologies to advance AI capabilities in key Estonian sectors, including e-governance, healthcare, business process management, and cybersecurity.

Contact: Rain Ottis, Pawel Maria Sobocinski, Tanel Alumäe, Tanel Tammet
Participating research and development institutions: Tartu University, Cybernetica AS, Tallinn University of Technology
Funder: Ministry of Education and Research
Period: 01.01.2024–31.12.2030
ETIS

The transition to Estonian-language education has increased interest in learning Estonian. While materials are available for learning grammar and vocabulary, there are limited resources for learning pronunciation. This project aims to create a pronunciation verifier tool for learners of Estonian (L2) and to develop the mobile application SayEst further. Currently, there are no automatic tools for assessing L2 pronunciation. Pronunciation is an important part of language learning, and clear pronunciation helps with better integration into society. Creating a pronunciation verifier will enable learners to receive immediate feedback on their pronunciation to learn at one's own pace in a stress-free environment. The L2 pronunciation verifier will provide feedback on each phoneme using a traffic light system. Additionally, we will develop an iOS version of SayEst in response to user feedback and also diversify the exercise content within SayEst.

Contact: Tanel Alumäe
Funder: Estonian Ministry of Education and Research
Period: 01.01.2024–31.12.2026
ETIS

We expect vocalists to sing with intelligible text, but singers also have to obey the constraints which are dictated by the music. Thus, the methods which are used to enhance diction in speaking may not necessarily be fully applicable to singing. The standpoints of singers regarding how to achieve clear pronunciation are controversial, and investigations on the subject are scarce. This project aims to create a scientific basis for the further development of strategies to achieve a good balance between intelligibility and the requirements of the music, such as cantilena and phrasing, when singing in various acoustics and with the presence of the accompaniment. The research method includes the acoustical analysis of the vocal performances and carrying out perception tests of vocal stimuli with systematically modified phonetic and musical parameters. The results are applicable to voice training and could help text writers and composers to reduce problems of text intelligibility.

Contact: Einar Meister
Funder: Estonian Research Council
Period: 01.01.2022–31.12.2026
ETIS

The project's goal is to add support for the Estonian language to selected open-source foundation language models, based on which it would later be possible to develop artificial intelligence applications that understand the Estonian language. Currently, support for the Estonian language is available in OpenAI's proprietary GPT models, the use of which is paid and which requires uploading the data into the OpenAI server. In addition, several open-source models exist that do not currently support the Estonian language. The project uses different training methods, full parameter training and parameter-efficient training, to add support for the Estonian language to the foundation models. In addition, the models will be fine-tuned on Estonian language instruction data and human ratings data to achieve better conversational ability. The project contributes to advancing language technological support for the Estonian language and the survival of the Estonian language in the digital age.

Contact: Tanel alumäe
Funder: Ministry of Education and Research
Period: 01.08.2024–31.12.2025
ETIS

Laboratory for Proactive Technologies

Head of Laboratory: JAANUS KAUGERAND, jaanus.kaugerand@taltech.ee

The laboratory focuses on theoretical and practical study of networked systems built from stationary and/or mobile software-intensive (proactive) components. Typical components are pervasive computing systems. The research is partitioned into three threads: (1) modelling and verification of situation-aware interaction-centred computation; (2) methods and technologies for acquiring situational information; (3) methods for interpretation of situational information for (proactive) decision making. The long-term goal of the laboratory is the ability to detect and partially control the emergent behaviour in pervasive computing systems.

In addition, ProLab performs research on classification, semantic segmentation and object detection using convolutional neural networks. The methodology has been applied to photographic images, point cloud collections and sound recordings.

Projects:

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.

Contact: Andres Udal
Partners: Ecole Royale Militaire - Koninklijke Militaire School, Gottfried Wilhelm Leibniz Universitaet Hannover, T4i Engineering Single Member Private Company, Ethniko Kentro Erevnas Kai Technologikis Anaptyxis, Bundesministerium Der Verteidigung, Exus Software Monoprosopi Etairia Periorismenis Evthinis, Interscience Bv
Funder: European Commission
Period: 01.12.2022–30.11.2025
ETIS

PI: Jaanus Kaugerand
Funder: TalTech
Period: 01.05.2024 - 31.10.2025

Next Gen Digital State Research Group

Head of the group: Ingrid PAPPEL, ingrid.pappel@taltech.ee

The Next Gen Digital State (NGDS) research group addresses the technological complexities of how governments can satisfy the current and future needs of their citizens. We focus on digital government ecosystems by investigating technologies that support digital transformation, innovation and implementation.

Our research group collaborates with Estonian and international public sector agencies, ministries, and departments for developing next-generation government-technology through cutting edge research topics focused on artificial intelligence architecture, requirements engineering, data analytics, and understanding the socio-economic effects of technological implementation. We strive to be on the forefront of public sector innovation research!

Projects:

Civic participation of marginalized youth is a central issue on policy agendas around the world. Recently, it has become even more important due to the pandemic, which, however, has also encouraged the acceleration of digitization of public services. EGOV4YOUTH intercepts these challenges by creating E-GOV skills and tools for local development aimed at facilitating collaborative youth/PA processes in decision making.

PI: Ingrid Pappel
Funder: Euroopa Komisjon
Period: 01.02.2024–31.07.2026

ETIS

PI: Ingrid Pappel
Funder: TalTech
Period: 01.07.2024 - 31.12.2025

Nonlinear Control Systems Group

Head of the research group: Tenured Assistant  Professor JURI BELIKOV, juri.belikov@taltech.ee

The group is a leading Estonian research unit in automatic control, focusing on nonlinear control systems, including non-smooth, hybrid and time-delay systems. The group has made a significant contributions to the development of constructive algebraic methods and the associated symbolic software package NLControl, which supports research, teaching and applications.

A universal algebraic methodology has been developed that simplifies the study of very different problems for nonlinear control systems from unified perspective. The main idea is to construct sequences of subspaces (or submodules) of differential 1-forms that provide a lot of information about the structural properties of the system. For instance, an event-based resource-aware control method based on the concept of differential flatness has been developed.

Although the group is developing predominantly application-independent general methods determined by the dynamic properties of the mathematical models, we have been recently focused on a few carefully chosen applications, some of them addressed within the joint topics in the Estonian Centre of Excellence on IT, our group is part of. These include control of autonomous underwater vehicles and ionic polymer-metal composite actuators. Within the last few years, special attention has been paid to the study of practical problems arising in limits of renewable energy integration, and determine the possible limitations of distributed energy storage devices in low inertia power systems utilizing methods from optimal control theory.

Projects:

CoE ENER covers 53% of final energy use in Estonia as well as major energy saving measures with highest investment volumes. CoE aims to contribute to Estonian societal and economic challenge to transform 75% of existing building stock with poor energy performance to zero emission buildings (ZEB) with maximized co-benefits and improved life quality by 2050. The scientific aim is to extend the excellence in ZEB technologies to become the top research centre in equity-enhancing deep renovation, driving disruptive changes and initiating systemic reforms encompassing innovative technologies, novel governance models, novel participatory and collaborative approaches to engage citizens. Interdisciplinary CoE combines engineering, social, data sciences and economics with central focus on energy performance of buildings and districts, electrification and flexibility, renewable energy generation and storage, energy saving measures and business models with their socioeconomic and regional impacts.

Contact: Juri Belikov
Funder: Estonian Ministry of Education and Research
Participating research and development institutions: Tallinn University of Technology, Tallinn University, University of Tartu
Period: 01.01.2024–31.12.2030
ETIS

Disturbances affect the performance of most technological systems. Rejection of the effects of disturbances is a key objective in control system design. A popular method for the disturbance rejection is to use the estimations of disturbances to compensate their effects. For this reason one has developed disturbance observers, which are used to find the estimates of the disturbances. However, majority of developed disturbance observers work under restrictive assumptions and thus cannot be used in most cases. The purpose of the project is to simplify the disturbance observer construction by studying possibilities to fulfill these restrictive assumptions. The main idea is to transform systems into forms for which the restrictive assumptions of the disturbance observer construction are satisfied. As a result, the construction of disturbance observers becomes much easier. This, in turn, helps to improve the performance of many popular control approaches.

PI: Arvo Kaldmäe
Funder: Estonian Research Council
Period: 01.01.2023–31.12.2027
ETIS

The major barrier for integration of renewable energy sources in modern power grids is impossibility to predict the impact of these sources on the system dynamics, stability, and control. A key challenge that distinguishes renewable energy sources from traditional generators is their fast dynamic responses and low inertia. Our plan is to develop a systematic framework towards modeling, analysis, and control design of complex systems with high level of renewable sources. To do so we will utilize the rich variety of mathematical tools developed recently in nonlinear control theory, including the algebraic framework of differential 1-forms and Hamiltonian structures.

PI: Juri Belikov
Funder: Estonian Research Council
Period: 01.01.2022–31.12.2026
ETIS