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Information Page of SAS Organisation

Project

Institute of Informatics

International Projects

iMagine - Imaging data and services for aquatic science (iMagine)

Cloudové služby pre spracovanie obrazových údajov pre vedy o vode

Duration: 1. 9. 2022 - 31. 8. 2025
Program: Horizont Európa
Project leader: Ing. Tran Dinh Viet PhD.
Annotation:iMagine provides a portfolio of free at the point of use image datasets, high-performance image analysis tools empowered with Artificial Intelligence (AI), and Best Practice documents for scientific image analysis. These services and materials enable better and more efficient processing and analysis of imaging data in marine and freshwater research, accelerating our scientific insights about processes and measures relevant for healthy oceans, seas, coastal and inland waters. By building on the computing platform of the European Open Science Cloud (EOSC) the project delivers a generic framework for AI model development, training, and deployment, which can be adopted by researchers for refining their AI-based applications for water pollution mitigation, biodiversity and ecosystem studies, climate change analysis and beach monitoring, but also for developing and optimising other AI-based applications in this field. The iMagine compute layer consists of providers from the pan-European EGI federation infrastructure, collectively offering over 132,000 GPU-hours, 6,000,000 CPU-hours and 1500 TB-month for image hosting and processing. The iMagine AI framework offers neural networks, parallel post-processing of very large data, and analysis of massive online data streams in distributed environments. 13 RIs will share over 9 million images and 8 AI-powered applications through the framework. Having representatives so many RIs and IT experts, developing a portfolio of eye-catching image processing services together will also give rise to Best Practices. The synergies between aquatic use cases will lead to common solutions in data management, quality control, performance, integration, provenance, and FAIRness, contributing to harmonisation across RIs and providing input for the iMagine Best Practice guidelines. The project results will be integrated into and will bring important contributions from RIs and e-infrastructures to EOSC and AI4EU.

EGI-ACE - EGI Advanced Computing for EOSC (EGI-ACE)

EGI pokročilé počítanie pre EOSC

Duration: 1. 1. 2021 - 30. 6. 2023
Program: Horizont 2020
Project leader: doc. Ing. Hluchý Ladislav CSc.
Annotation:The mission of the EGI-ACE project of EGI is to implement the Compute Platform of the European Open Science Cloud, by delivering a secure federation of Cloud compute and storage facilities in collaboration with providers of the EGI Federation, commercial providers, data providers and international research infrastructures of pan-European relevance.
Project web page:https://www.egi.eu/projects/egi-ace/

SILVANUS - Integrated Technological and Information Platformfor wildfire Management (SILVANUS)

Integrovaná technologická a informačná platforma pre manažment lesných požiarov

Duration: 1. 10. 2021 - 31. 3. 2025
Program: Horizont 2020
Project leader: Ing. Balogh Zoltán PhD.
Annotation:SILVANUS envisages to deliver an environmentally sustainable and climate resilient forest management platform through innovative capabilities to prevent and combat against the ignition and spread of forest fires. The platform will cater to the demands of efficient resource utilisation and provide protection against threats of wildfires encountered globally. The project will establish synergies between (i) environmental; (ii) technology and (iii) social science experts for enhancing the ability of regional and national authorities to monitor forest resources, evaluate biodiversity, generate more accurate fire risk indicators and promote safety regulations among citizens through awareness campaigns. The novelty of SILVANUS lies in the development and integration of advanced semantic technologies to systematically formalise the knowledge of forest administration and resource utilisation. Additionally, the platform will integrate a big-data processing framework capable of analysing heterogeneous data sources including earth observation resources, climate models and weather data, continuous on-board computation of multi-spectral video streams. Also, the project integrates a series of sensor and actuator technologies using innovative wireless communication infrastructure through the coordination of aerial vehicles and ground robots. The technological platform will be complemented with the integration of resilience models, and the results of environmental and ecological studies carried out for the assessment of fire risk indicators based on continuous surveys of forest regions. The surveys are designed to take into consideration the expertise and experience of frontline fire fighter organisations who collectively provide support for 47,504x104 sq. meters of forest area within Europe and across international communities. The project innovation will be validated through 11 pilot demonstrations across Europe and internationally using a two sprint cycle.

SWORD - Smart Wound monitoring Restorative Dressings

Inteligentné diagnosticko-terapeutické náplasti

Duration: 1. 1. 2020 - 30. 6. 2024
Program: Horizont 2020
Project leader: RNDr. Bardošová Mária CSc.
Annotation:The project aims to combine the expertise and resources of four academic groups working at the frontiers of material research and two SMEs, one engaged in producing commercial medical materials as well as having own research capacities. The Consortium plans to develop wound dressings which an be rated as a radical innovation in the area of chronic wound care. Our efforts will concentrate on the design of a new product which will combine diagnostic and therapeutic actions and facilitate wound healing, while at the same time minimize treatment costs and optimize wound management thus positively influencing patient's wellbeing. A range of new composites derived from naturally occurring materials, such as chitin and halloysite combined with colloids will be studied. The ensuing hybrid materials will be processed using micro- and nano-manipulating tools, including roll-to-roll and electrospinning technologies. A thorough characterization program will be undertaken in order to understand the molecular-level interactions between the composite components. The determination of materials structure will allow a complete understanding of how their properties originate so that they can be tailored to precisely fit their application. The dressing will feature diagnostic sensors capable of measuring pH, humidity, temperature and associated bacterial activity as well as a highly absorbent layer designed to drain the wound and release therapeutic agents. Each member of the Consortium is well-resourced in terms of facilities, infrastructure and technology transfer support so as to provide an ideal environment for the research program, the training of younger researchers and the exploitation of findings. Leveraging from nationally funded grants and actively engaging in research exchanges while seeking to produce a radical innovation in the area of 'smart' dressings the project will address all three key drivers of the knowledge-based society, namely research, education an innovation.
Project web page:https://cordis.europa.eu/project/id/873123

COBRA - COnversational BRAins

Konverzujúce mozgy

Duration: 1. 2. 2020 - 31. 1. 2024
Program: Horizont 2020
Project leader: Prof.Mgr. Beňuš Štefan PhD.
Annotation:The EU-funded COBRA project will train the next generation of researchers to accurately characterise and model the linguistic and cognitive brain mechanisms that allow conversation to unfold in both human-human and human-machine interactions. The first challenge will be to determine how alignment and prediction may both rely on and contribute to setting up brain-to-brain coupling relationships. The second will relate to the development of computational models of alignment and prediction for more socially-acceptable text-to-speech synthesisers, human-machine dialogue systems, and social robots.
Project web page:https://www.cobra-network.eu/the-project/

CPSforTM - Cyber-physical systems (CPS), Internet of Things (IoT), wireless sensor networks, Smart monitoring, tele-medicine, COVID-19

Kyberneticko-fyzický systém pre epidemiologický tele-monitoring a tele-medicínu pre pacientov s COVID 19

Duration: 1. 1. 2021 - 31. 12. 2022
Program: Mobility
Project leader: Ing. Budinská Ivana PhD.
Annotation:The aim of the project is research and development of Cyber-Physical System (CPS) for tele-monitoring and tele-medicine in hospital care during pandemic situations, including the following embedded systems: mechanical system, hardware, software, sensors, IoT devices, communication system and web-based user interface containing the necessary functions for work in the hospital environment. This complex system will be controlled via the Internet. The cyber-physical system for epidemiological monitoring in hospital care will be built by a specialized modular Robot-Assistant, and IoT devices together with a system for a seamless communication and aggregation of sensory and multimodal data for tele-medicine and tele-monitoring of the patients with Covid 19. Thus, the physical contact between the medical staff with patients will be limited. Moreover, the risk of repeated daily exposure to corona virus for physicians during each manipulation will be greatly reduced. The project will boost up the cooperation of two academic institutions with complementary research activities. Participating PhD students and young researchers will gain experience from international cooperation, from which they will benefit in their further professional career.

Development of machine learning models for high-performance computing

Návrh modelov strojového učenia pre vysoko-výkonné počítanie.

Duration: 1. 4. 2020 - 31. 12. 2022
Program: Medziakademická dohoda (MAD)
Project leader: doc. Ing. Hluchý Ladislav CSc.
Annotation:Nowadays, Machine learning (ML) is important and relevant trend in the development of modern computer science. ML is not new concept it was part of the field of artificial intelligence for a long time. The main idea focuses on building algorithms that can learn on their own, but recent advances in computing systems and the availability of big data create impressive progress from checkers solving program to self-moved cars and programs that recognize single face from thousands of photos. One of the applications of ML is to improve the efficiency of computations by tuning parameters of environment. Here we deal with complex computational program as with black box, which dynamic depends on parameters. In this project we propose to investigate process of auto tuning of parallel program on heterogeneous multiprocessor system. This problem is important and has some features, that makes it hard for solving. First of all, data and environment can influence performance of program in crucial way. Also, in some setups algorithm can control scheduling making strategy time-dependant. Secondly, an important feature is the presence of competing users. It is necessary to ensure fair and equal access to resources. Because each user is a rational agent and seeks to increase his or her share of the resource at the expense of others, an unbalanced distribution algorithm can shift the system to an inefficient equilibrium. This is problem of game theory. There are notable connections of ML methods with game theory. For example we can mention GANs (generative Adversarial Nets) corresponding to a two-player game, SVM (support vector machine) connected with zero-sum two-player game, and others. This project proposes the following approaches to address the problem. The first is model-free reinforcement machine learning, which is trying to find the best possible control strategy. Secondly, it is the application of the game-theoretic approach, in which the formalized user behavior is involved in the system. Each user has their own learning algorithm and is a rational agent who wants to get some of the computing resource and maximize its utility function. The project continues previous collaboration between institutes in the design of adaptive programming methods for high-performance computing in heterogeneous multiprocessor environments. Project objectives: The purpose of the project is to analyze and synthesize new machine learning algorithms in concurrent high-performance computing. Existing machine learning algorithms are usually heuristic solutions, the quality of which depends on the data and training parameters. One of the promising areas of better understanding of the work is the application of game-theoretical approaches to modeling the learning process of competing algorithms. The results of the game analysis will help to describe the optimal behavior of users at the point of equilibrium and to calculate the characteristics of schedulers and translation algorithms.

Semiconducting Metal Oxide - New Materials For Environmental Sensors

Polovodivé oxidy kovov – nové materiály pre environmentálne senzory

Duration: 1. 1. 2021 - 31. 12. 2022
Program: Mobility
Project leader: Ing. Mgr. Andok Robert PhD.
Annotation:This project proposal is motivated by the research of new semiconducting metal oxides materials such as titanium dioxide TiO2 ,NiO, ZnO, as well as from the present state of the development of the electron beam lithography (EBL) which is one of the alternative methods of patterns preparation. The project proposal is aimed at obtaining new scientific knowledge in the patterning of nanometer scale structures (50-150 nm) in electron beam resists on thin films of semiconducting metal oxides. The attention will be paid to the research of the influence of the electron lithography processes on the resulting nanometer structures patterned in electron beam resists on thin film sof semiconducting metal oxides in term of resolution, accuracy of the dimension of structures in resist and the side wall shapes of the structures in the electron beam resist. The simulations of lithographic parameters of electron bem resists on thin semiconducting metal oxide layers, present an important part of this project. Based on the obtained know-how and simulations of lithographic parameters, we will prepare nanometer patterns on thin semiconducting metal oxide layers including TiO2 for chemoresistivegas sensors development.

FIRElinks - Fire in Earth System: Science & Society (FIRElinks)

Požiar v systéme Zeme: veda a spoločnosť

Duration: 1. 6. 2019 - 1. 6. 2023
Program: COST
Project leader: RNDr. Glasa Ján CSc.

TREX - Targeting Real Chemical Accuracy at the Exascale (TREX)

Targeting Real Chemical Accuracy at the Exascale

Duration: 1. 6. 2021 - 30. 9. 2023
Program: Horizont 2020
Project leader: Prof. Ing. Štich Ivan DrSc.

AI4EOSC - Artificial Intelligence for the European Open Science Cloud (AI4EOSC)

Umelá inteligencia pre EOSC

Duration: 1. 9. 2022 - 31. 8. 2025
Program: Horizont Európa
Project leader: Ing. Tran Dinh Viet PhD.
Annotation:The AI4EOSC (Artificial Intelligence for the European Open Science Cloud) delivers an enhanced set of advanced services for the development of Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL) models and applications in the European Open Science Cloud (EOSC). These services are bundled together into a comprehensive platform providing advanced features such as distributed, federated and split learning; novel provenance metadata for AI/ML/DL models; event-driven data processing services or provisioning of AI/ML/DL services based on serverless computing. The project builds on top of the DEEP-Hybrid-DataCloud outcomes and the EOSC compute platform and services in order to provide this specialized compute platform. Moreover, AI4EOSC offers customization components in order to provide tailor made deployments of the platform, adapting to the evolving user needs. The main outcomes of the AI4EOSC project will be a measurable increase of the number of advanced, high level, customizable services available through the EOSC portal, serving as a catalyst for researchers, facilitating the collaboration, easing access to high-end pan-European resources and reducing the time to results; paired with concrete contributions to the EOSC exploitation perspective, creating a new channel to support the build-up of the EOSC Artificial Intelligence and Machine Learning community of practice.
Project web page:https://ai4eosc.eu/

EuroScienceGatew - leveraging the European compute infrastructures for data-intensive research guided by FAIR principles (EuroScienceGateway)

využitie európskych výpočtových infraštruktúr pre výskum náročný na údaje riadený zásadami FAIR

Duration: 1. 9. 2022 - 31. 8. 2025
Program: Horizont Európa
Project leader: Ing. Tran Dinh Viet PhD.
Annotation:In the past decade, many scientific domains have been transformed into data-driven disciplines relying on the exchange and integration of internationally distributed data. Exploiting this data is still a laborious and largely manual task, prone to losses and errors, and increasingly specialised beyond most users technical capabilities. FAIR practices are encouraged but their adoption curve is steep. The needs for compute and data resources, tools, and application platforms are often domain-specific. Many scientists struggle to navigate this intricate ecosystem. Generally, researchers do not possess the computing skills to effectively use the HPC or Cloud platforms they need. Thus, new approaches are needed to enable all researchers, with widely ranging digital skills, to efficiently use the diverse computational infrastructures available across Europe, for asynchronous and for interactive applications. EuroScienceGateway will leverage a distributed computing network across 13 European countries, accessible via 6 national, user-friendly web portals, facilitating access to compute and storage infrastructures across Europe as well as to data, tools, workflows and services that can be customized to suit researchers' needs. At the heart of the proposal workflows will integrate with the EOSC-Core. Adoption, development and implementation of technologies to interoperate across services, will allow researchers to produce high-quality FAIR data, available to all in EOSC. Communities across disciplines - Life Sciences, Climate and Biodiversity, Astrophysics, Materials science - will demonstrate the bridge from EOSC's technical services to scientific analysis. EuroScienceGateway will deliver a robust, scalable, seamlessly integrated open infrastructure for data-driven research, contributing an innovative and customizable service for EOSC that enables operational open and FAIR data and data processing, empowering European researchers to embrace the new digital age of science.

National Projects

ARIEN - Analysis of environmental influences on power industry equipment by the methods of artificial intelligence and cloud computing (ARIEN)

Analýza vplyvu prostredia na zariadenia energetického priemyslu metódami umelej inteligencie a cloudového počítania

Duration: 1. 7. 2021 - 31. 12. 2023
Program: APVV
Project leader: Ing. Kvassay Marcel PhD.
Annotation:The main goal of project ARIEN is to develop & verify a new innovative method for the estimation of local pollution deposition for power industry needs based on the data collected by the SHMU Institute. It will rely on advanced techniques of artificial intelligence, cloud computing and data integration and was not yet applied in Slovak power industry. It will help to prevent outages on 110 kV and 400 kV overhead lines caused by flashovers. The results obtained by this method will help to optimize the design of insulators and improve the quality of maintenance during operation. Artificial intelligence is the logical choice, because so far we have not been able, due to their complexity, to describe the processes of pollution dispersion, deposition and chemical interaction adequately by standard physical and chemical models. Techniques of artificial intelligence and neural networks can find new relationships between the input and the output data. Neural networks, however, require large training sets. Therefore, the second project goal is to develop a method for obtaining a sufficiently large set of synthetic data for their training and testing. The third project goal is to research & develop a new method for measuring the insulator pollution by the analysis of their acoustic emissions captured by contactless detectors placed in their vicinity. Here we rely on the experience of the project partner VUJE with measurement systems installed on overhead lines and able to work during their normal operation. These goals reflect current priorities of European research as well as pressing needs of Slovak power industry: in 2015, the standard STN 33 0405 regulating the assessment of high-voltage insulator pollution deposition and their required cleaning frequency, was withdrawn. The new methodology will help replace the tedious & demanding process mandated by this standard by a new one relying on readily available information and the latest computing techniques.

EWA - Early Warning of Alzheimer

Early Warning of Alzheimer

Duration: 1. 9. 2020 - 31. 8. 2023
Program: Štrukturálne fondy EÚ Bratislavský kraj
Project leader: Ing. Rusko Milan PhD.

ICONTROL - Intelligent Cloud Workflow Management for Dynamic Metric- Optimized Application Deployment (ICONTROL)

Inteligentné riadenie tokov práce v cloude pre dynamické a metrikami optimalizované nasadzovanie aplikácií

Duration: 1. 7. 2021 - 31. 12. 2023
Program: APVV
Project leader: doc. Ing. Hluchý Ladislav CSc.
Annotation:ICONTROL is a platform for dynamic and intelligent function-based workflow application deployment and run-time redeployment in hybrid edge cloud computing environments. ICONTROL will automatically construct and deploy complex workflow-based cloud applications in an end-to-end manner. ICONTROL will use semantic information on available cloud-based application functionalities to automatically generate complex workflows of functions, which will be executed across edge and cloud resources. It will help users to automate function selection, configuration and deployment, dealing with run-time failure or performance issues through automated redeployment. To remove obstacles for non-cloud expert users to use complex function-based workflow applications, ICONTROL will support a separation of tasks among 3 categories of specialists. Cloud application developers will develop application and backend functions, semantically annotate and describe them. AI-powering algorithms will assist domain application experts to create application workflows using functionalities provided by the cloud application developers, with semi-automated workflow construction techniques by leveraging semantic descriptions. Application users will be able to use their application workflows to instantiate, deploy and run their applications using the automation capabilities of the proposed system in complex hybrid edge and cloud environments, not burdened by complexities related to workflows selection, application deployment, fault resolution, resource elasticity provision, to name just a few. ICONTROL will significantly improve composability and adaptability of workflow-based applications spanning the entire edge-cloud continuum (from remote wireless IoT sensors, through personal devices, to large computing centers), by creating a semantically-enriched, platform-agnostic, and secured FaaS workflow development and execution platform, with automatic infrastructure resource management and provisioning.

Manipulation of spin properties in 2D materials

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Duration: 1. 9. 2022 - 31. 8. 2024
Program: SASPRO
Project leader: Prof. Ing. Štich Ivan DrSc.

RFMEMS - Microelectromechanical sensors with radio frequency data transmission

Mikroelektromechanické senzory s rádiofrekvenčnýcm prenosom údajov

Duration: 1. 7. 2021 - 30. 6. 2025
Program: APVV
Project leader: Ing. Havlík Štefan DrSc.
Annotation:The project elaborates the method proposed by the authors, especially of mechanical quantities with wireless signal / energy transmission via electromagnetic field, solution of sensors as well as (micro) electro-mechanisms (MEMS). The task is a logical continuation of the results achieved within the successful solution of the previous project APVV 14-0076, where the principle of scanning and conception of the sensor solution according to the original design (utility model 8653, published patent applications PP 121-2018) was designed and verified. The presented project represents further theoretical and methodological-experimental processing in order to meet specific requirements for the solution of specific sensors including compliant - deformation members and electronic evaluation circuits as well as other electro-mechanisms with respect to selected applications. Part of the solution is to create tools for modeling, simulation and optimization of properties using available MEMS technologies. The project aims to follow the latest global trend in MEMS solutions.
Project web page:-

- - Modelling and supervisory control of resource allocation systems in discrete-event systems using of Petri nets

Modelovanie a supervízorové riadenie systémov prideľovania zdrojov v udalostných systémoch pomocou Petriho sietí

Duration: 1. 1. 2021 - 31. 12. 2024
Program: VEGA
Project leader: Doc. Ing. Čapkovič František CSc.
Annotation:Discrete-Event Systems (DES) are systems that remain in a given state until they are forced to change this state due to the occurrence of a discrete event. In practice, e.g. flexible manufacturing systems (FMS), robot cells, transport and communication systems, etc. are DES. Resource Allocation Systems (RAS) in DES tend to deadlocks. Deadlocks must be eliminated. Petri Nets (PN) will be used to model RAS and synthesize their control to avoid deadlocks. Two approaches to the supervisor synthesis will be explored: (i) on the basis of P-invariants of PN, at the simultaneous thorough analysis of the reachability tree (RT) of PN and at knowledge of the initial state of the PN model; (ii) by the thorough analysis of the PN model stucture and finding siphons and traps without the need to know the initial state. These two approaches will be compared. Their effectiveness and their advantages and disadvantages will be evaluated using the simulation in the Matlab environment.

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Nanoštruktúrne polovodivé materiály a ich integrácia do chemoodporových senzorov plynov a do senzorov ťažkých kovov

Duration: 1. 1. 2021 - 31. 12. 2024
Program: VEGA
Project leader: RNDr. Kostič Ivan

Low-dimensional materials - manipulation, functionalisation and bioapplications: LOW-D-MATTER

Nízko-dimenzionálne materiály- manipulácia, funkcionalizácia a bioaplikácie: LOW-D-MATTER

Duration: 1. 1. 2021 -
Program: VEGA
Project leader: Prof. Ing. Štich Ivan DrSc.

New Methods and Approaches for Distributed Scalable Computing

Nové metódy a prístupy pre distribuované škálované počítanie

Duration: 1. 1. 2020 - 31. 12. 2022
Program: VEGA
Project leader: doc. Ing. Hluchý Ladislav CSc.
Annotation:Nowadays, there is a growing number of heterogeneous data from distributed sources, which brings great challenges to extract valuable knowledge from them. Current solutions are computationally-intensive modeling and simulations in a variety of science, industry and commercial areas through machine learning, neural networks, and deep learning. Due to the extremely multi-faceted dynamic data it is highly necessary to design novel methodologies, robust methods and approaches for scalable analytics in conjunction with scalable data collection, processing and management. High-performance platforms also need to be upgraded with the latest cloud technology knowledge for flexible management of large-scale systems. The project will also include research and development of appropriate tools and services for distributed and scalable information processing with the support for high-performance platforms. The proposed project builds on the results achieved in the VEGA 2/0167/16 project and in the four H2020 projects.

ORBIS - Ontology representation for security of information systems

Ontologická reprezentácia pre bezpečnosť informačných systémov

Duration: 1. 7. 2020 - 30. 6. 2024
Program: APVV
Project leader: Ing. Budinská Ivana PhD.
Annotation:According to the action plan of the government of the Slovak Republic, one of the priorities is to “support research in cyber security”. This project proposal responds to the action plan's requirements by setting research objectives and goals in the field of cybersecurity. The project proposal focuses on new approaches in sharing knowledge about security incidents and indicators. There are several initiatives that seek to formalize and standardize security incidents descriptions. However, it is neither realistic nor desirable to assume that there will be one common standard for describing security incidents. The solution can be offered by creating a core ontology, which semantically integrates various approaches to describing threats and attacks, thus enabling the integration of several standards and knowledge repositories. The tools for representation and processing of ontologies will be used for this purpose. It will be a combination of procedures for collecting data from network communication, creating the necessary ontologies eg. semiautomatic extraction of ontologies from text in natural language – ontology learning, use of selected advanced methods of ontological knowledge representation, e.g. contextualized representation or methodology of ontological metamodeling. A significant shift will be that the ontology of the model can be represented by a graph. The results can be expected to contribute to more efficient sharing, representation, storage, and use of cybersecurity knowledge.

Computer modelling of fire dynamics and effects

Počítačové modelovanie dynamiky požiaru a jeho dôsledkov

Duration: 1. 1. 2020 - 31. 12. 2022
Program: VEGA
Project leader: RNDr. Glasa Ján CSc.
Annotation:Research in the suggested project is oriented on formulation of new scientific knowledge in the field of computer modelling of fire dynamics and effects. Huge societal damages on property, environment, destroyed buildings and infrastructure and threat for human lives and health caused by fires create acute need for such research of all-society importance. The research will utilize the authors‘ results and experiences on experimental testing of fires and investigation of applicability of models and program systems capable to simulate complex processes related to fire. The research will focus on computer simulation of fire course and consequences of chosen fire scenarios and its efficient parallel realization on high-performance computer systems.

SenArtI - Processing of sensor data via Artificial Intelligence methods.

Spracovanie údajov zo senzorov prostriedkami umelej inteligencie

Duration: 1. 1. 2019 - 31. 12. 2022
Program: VEGA
Project leader: Ing. Malík Peter PhD.
Annotation:The project will propose new methods and algorithms for processing of multi-sensor data for tasks of object diagnostics, areas of interest evaluation, secure communication and simplification of the new intelligent models creation. The research will focus on advanced methods of artificial intelligence with emphasis on deep learning. Artificial Intelligence algorithms show significantly better results than traditional methods, and an example of this is the tremendous progress of refining semantic image segmentation through deep learning over the last five years. Modern, low-cost, miniature electro-mechanical structures allow simple integration and group deployment of multiple sensors, resulting in the production of a huge amount of multi-sensor data that can not be manually processed. The project output will include new models of accurate semantic image segmentation, precision object modeling, multiple sensor collaboration models, and secure data transfer, especially for the Internet of Things and Industry 4.0.

2DQMC - -

Štúdium elektrónových vlastností 2D materiálov ultra-presnými metódami kvantového Monte Carla

Duration: 1. 7. 2022 - 30. 6. 2025
Program: APVV
Project leader: Prof. Ing. Štich Ivan DrSc.

Automatic speech processing technologies for support in crisis situations

Technológie automatického spracovania reči na pomoc v krízových situáciách

Duration: 1. 1. 2021 - 31. 12. 2024
Program: VEGA
Project leader: Prof.Mgr. Beňuš Štefan PhD.

AIPOLOGY - Artificial Intelligence for Personalised Oncology: from Single-Sample Assessment to Real-time Monitoring of Solid Tumours (AIPOLOGY)

Umelá inteligencia pre precíznu onkológiu: od analýzy jednotlivých vzoriek po real-time monitorovanie progresie nádorových ochorení.

Duration: 1. 7. 2022 - 30. 6. 2025
Program: APVV
Project leader: doc. Ing. Hluchý Ladislav CSc.
Annotation:The methodologies that oncologists use to decide on a patient's treatment are ever changing. It seems to us that 21st century cancer medicine is much about analysing big data and using mathematical modelling to extract information that can help predict how tumours will evolve and react to potential therapies. The sad fact is, however, that despite ever increasing knowledge on cancer we still lack the proper tools to translate this knowledge to an impactful “bedside” practice that would overcome the limitation from cancer heterogeneity and allow real-time monitoring of disease progression. Here, we propose the AIpology project that aims at the development of novel artificial intelligence strategies to identify molecular traits (individual mutations, mutation signatures and genomic scars) in heterogeneous cancer genomes for which therapeutic targets exist. Based on target clonal mapping and ordering, the system will then outline possible courses of treatment and will intelligently adapt as more data from real time monitoring approaches (such as liquid biopsy) will become available. The system will help us to track each target at the finer time scale than it is possible today and predict future (i.e how the tumour will evolve after being treated with a specific drug) and past (i.e. how long the tumour existed prior to detection) cancer evolutionary trajectories from existing data. Finally, we will understand better why certain cancers become (chemo)therapy-resistant and derive clinically relevant recommendations when they do.

HYSPED - (HYSPED)

Výskum aplikácie prostriedkov umelej inteligencie pri analýzach a klasifikácií dát hyperspektrálneho snímkovania

Duration: 1. 2. 2022 - 30. 9. 2023
Program: Európsky fond regionálneho rozvoja (EFRR)
Project leader: doc. Ing. Hluchý Ladislav CSc.

Projects total: 28