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International projects
| MU training – Measurement uncertainty training – MATHMET project to improve quality, efficiency and dissemination of measurement uncertainty training | |
| Tréning v oblasti neistôt merania – MATHMET projekt na zlepšenie kvality, efektívnosti a šírenia zručností v oblasti analýzy neistoty výsledkov merania | |
| Program: | Multilaterálne – iné |
| Duration: | 1.10.2021 – 30.9.2023 |
| Project leader: | Doc. RNDr. Witkovský Viktor, CSc. |
| Annotation: | Measurement uncertainty is a key quality parameter to express the reliability of measurements and an understanding of measurement uncertainty is often a precondition for advances in science, industry, health, environment, and society in general. However, there is a documented need for a better understanding of measurement uncertainty and its evaluation in many communities and recently this need was restated pointing to the importance of training on measurement uncertainty. Many metrology institutes, universities, national accreditation bodies, authorities in legal metrology, and others offer training on measurement uncertainty. They do so independently, and there is no community of teachers for exchanging expertise or to focus attention. There is no single contact point in Europe, which coordinates efforts, prioritizes needs, or provides an overview of suitable courses and material.Based on a broad consortium this project will improve the quality, efficiency, and dissemination of measurement uncertainty training. The activity will (1) develop new material for measurement uncertainty training and (2) establish an active community for those involved in measurement uncertainty training. In the EU, the European Metrology Network MATHMET is well-suited to host such an activity. |
| Project website: | https://www.euramet.org/european-metrology-networks/mathmet/?L=0 |
| ReHaB – Towards an ecologically valid symbiosis of BCI and head-mounted VR displays: focus on collaborative post-stroke neurorehabilitation | |
| Smerovanie k spoľahlivej a uživateľsky prijateľnej symbióze BCI a VR: zameranie na kolaboratívnu neurorehabilitáciu po cievnej mozgovej príhode | |
| Program: | ERANET |
| Duration: | 1.1.2022 – 31.12.2024 |
| Project leader: | Ing. Mgr. Rosipal Roman, DrSc. |
| Annotation: | A growing body of evidence suggests that integrated technologies of brain-computer interfaces (BCI) and virtual reality (VR) environments provide a flexible platform for a series of neurorehabilitation therapies, including significant post-stroke motor recovery and cognitive-behavioral therapy. When immersed in such an environment, the subject\’s perceptual level of social interaction is often impaired due to the sub-optimal quality of the interface lacking the social aspect of human interactions.The project proposes a user-friendly wearable low-power smart BCI system with an ecologically valid VR environment in which both the patient and therapist collaboratively interact via their person-specific avatar representations. On the one hand, the patient voluntarily, and in a self-paced manner, manages their activity in the environment and interacts with the therapist via a BCI-driven mental imagery process. This process is computed and rendered in real-time on an energy-efficient wearable device. On the other hand, the therapist\’s unlimited motor and communication skills allow him to fully control the environment. Thus, the VR environment may be flexibly modified by the therapist allowing for different occupational therapy scenarios to be created and selected following the patient\’s recovery needs, mental states, and instantaneous responses. |
National projects
| MRCartilage – Automatic data evaluation tool from the longitudinal quantitative MRI studies of articular cartilage | |
| Automatický softvérový nástroj na výhodnocovanie kvantitatívnych MRI štúdií artikulárných chrupaviek v čase | |
| Program: | APVV |
| Duration: | 1.7.2022 – 30.6.2026 |
| Project leader: | Ing. Dr. Szomolányi Pavol, (PhD.) |
| Annotation: | The aim of the project is to design a comprehensive tool for automatic evaluation of human articular cartilage data from quantitative MRI. Data obtained from the Osteoarthritis Initiative database, and measured at Institute of Measurement Science and Medical University of Vienna will be segmented using an automated segmentation tool based on convolutional neural networks. The annotated data will then be registered on quantitative MRI data that will be available from the database (T2 and T1rho mapping, gagCEST, sodium MR) using automated or semiautomated tools developed within this project. The data obtained will be evaluated at multiple time points according to MR measurements that will be available. In addition to quantitative MR data, this will include volumetric data, cartilage thickness, and texture analysis of quantitative maps. Patient evaluation will be based on risk factor groups (transverse ligament rupture, meniscus rupture and menisectomy). The expected number of patients is approximately 4000 divided into individual groups in the ratio 40/30/30. The output of the project will be a compiled version of an automatic cartilage evaluation tool that will be available in a public source (such as website of Institute of Measurement). |
| Causal analysis of measured signals and time series | |
| Kauzálna analýza nameraných signálov a časových radov | |
| Program: | VEGA |
| Duration: | 1.1.2022 – 31.12.2025 |
| Project leader: | RNDr. Krakovská Anna, CSc. |
| Annotation: | The project is focused on the causal analysis of measured time series and signals. It builds on the previous results of the team, concerning the generalization of the Granger test and the design of new tests in the reconstructed state spaces. The aim of the project is the development of new methods for bivariate and multidimensional causal analysis. We will see the investigated time series and signals as one-dimensional manifestations of complex systems or subsystems. We will also extend the detection of causality to multivariate cases – dynamic networks with nodes characterized by time series. Such complex networks are common in the real world. Biomedical applications are among the best known. Brain activity, determined by multichannel electroencephalographic signals, is a crucial example. We want to help show that causality research is currently at a stage that allows for ambitious goals in the study of effective connectivity (i.e., directed interactions, not structural or functional links) in the brain. |
| MATHMER – Advanced mathematical and statistical methods for measurement and metrology | |
| Pokročilé matematické a štatistické metódy pre meranie a metrológiu | |
| Program: | APVV |
| Duration: | 1.7.2022 – 31.12.2025 |
| Project leader: | Doc. RNDr. Witkovský Viktor, CSc. |
| Annotation: | Mathematical models and statistical methods for analysing measurement data, including the correct determination of measurement uncertainty, are key to expressing the reliability of measurements, which is a prerequisite for progress in science, industry, health, the environment and society in general. The aim of the project is to build on traditional metrological approaches and develop new alternative mathematical and statistical methods for modelling and analysing measurement data for technical and biomedical applications. The originality of the project lies in the application of modern mathematical methods for modelling and detecting dependence and causality, as well as statistical models, methods and algorithms for determining measurement uncertainty using advanced probabilistic and computational methods based on the use of the characteristic function approach (CFA). In contrast to traditional approximation and simulation methods, the proposed methods allow working with complex and at the same time accurate probabilistic measurement models and analytical methods. Particular emphasis is placed on stochastic methods for combining information from different independent sources, on modelling dependence and causality in dynamic processes, on accurate methods for determining the probability distribution of values that can be reasonably attributed to the measured quantity based on a combination of measurement results and expert knowledge, and on the development of methods for comparative calibration, including the probabilistic representation of measurement results with a calibrated instrument. An important part of the project is the development of advanced numerical methods and efficient algorithms for calculating complex probability distributions by combining and inverting characteristic functions. These methods are widely applicable in various fields of measurement and metrology. In this project they are applied to the calibration of temperature and pressure sensors. |
| Probability distributions and their applications in modelling and testing | |
| Rozdelenia pravdepodobnosti a ich aplikácie v modelovaní a testovaní | |
| Program: | VEGA |
| Duration: | 1.1.2021 – 31.12.2023 |
| Project leader: | Doc. RNDr. Witkovský Viktor, CSc. |
| Annotation: | The project is focused on the research of complex problems related to probability distributions and their use in mathematical modeling and tests of statistical hypotheses, where it is necessary to know the distributions of test statistics. A new broad family of probability distributions will be investigated. Many commonly used distributionsare special cases of this family. New approaches to multivariate statistical problems will be developed (an error-in-variables linear model, nonparametric statistical inference about several populations, nonparametric independence tests, detection of causality). A new apparatus of mathematical statistics will be applied to mathematical models in metrology, linguistics, demography, and insurance mathematics. |
| TInVR – Trustworthy human–robot and therapist–patient interaction in virtual reality | |
| Dôveryhodná interakcia človek–robot a terapeut–pacient vo virtuálnej realite | |
| Program: | APVV |
| Duration: | 1.7.2022 – 30.6.2026 |
| Project leader: | Ing. Mgr. Rosipal Roman, DrSc. |
| Annotation: | We aim to study specific forms of social interaction using state-of-the-art technology – virtual reality (VR) which is motivated by its known benefits. The project has two main parts, human–robot interaction (HRI) and therapist–patient interaction (TPI). The interactions are enabled using head-mounted displays and controllers allowing the human to act in VR. We propose two research avenues going beyond the state-of-the-art in respective contexts. In HRI, we will develop scenarios allowing the humanoid robot to learn, understand and imitate human motor actions using flexible feedback. Next, we develop scenarios for testing and validating human trust in robot behavior based on multimodal signals. We will also investigate physical interaction with a humanoid robot NICO. In TPI with stroke patients, we develop a series of VR-based occupational therapy procedures for motor and cognitive impairment neurorehabilitation using an active and passive brain-computer interface, and we will validate these procedures. We expect observations from HRI experiments to be exploited in TPI. The proposed project is highly multidisciplinary, combining knowledge and research methods from psychology, social cognition, robotics, machine learning and neuroscience. We expect to identify features and mechanisms leading to trustworthy processes with a human in the loop, as a precondition of success, be it a collaborative task or treatment in VR. |
| Smart deep brain stimulation as a treatment strategy in treatment-resistant depression | |
| Inteligentná hĺbková mozgová stimulácia ako inovatívna stratégia pre liečbu mozgových porúch | |
| Program: | VEGA |
| Duration: | 1.1.2022 – 31.12.2025 |
| Project leader: | Ing. Mgr. Rosipal Roman, DrSc. |
| Annotation: | Impaired connectivity between different brain areas underlines the pathophysiology of multiple brain disorders. It is possible that impaired connectivity between the prefrontal cortex and ventral pallidum is involved in depression. Smart deep brain simulation, combining real-time detection of the neuronal activity in the prefrontal cortex with the stimulation of the ventral tegmental area might be thus effective in depression. We aim to examine the cortico-tegmental connectivity and to test the antidepressant-like effectiveness of the smart deep brain stimulation in an animal model of depression. |
| ECMeNaM – Efficient computation methods for nanoscale material characterization | |
| Efektívne výpočtové metódy pre charakterizáciu materiálov v nano mierke | |
| Program: | APVV |
| Duration: | 1.7.2022 – 30.6.2025 |
| Project leader: | Doc. RNDr. Witkovský Viktor, CSc. |
| Annotation: | The aim of the project is to design and implement effective calculation methods for evaluating the results of measuring the mechanical properties of materials at the nanoscale using instrumented indentation methods (IIT) and atomic force microscopy (AFM). Both of these methods are able to provide highly localized information on the mechanical properties of the material, such as Young\’s modulus of elasticity (both methods), hardness (IIT method), or point-to-surface adhesion (AFM method). The principle is the analysis of the recording of the position of the measuring tip and the force interaction between the tip and the sample surface. The determination of the resulting values on the basis of data recorded by the instrument in both of these methods is based on non-trivial mathematical-statistical methods and calculation procedures working with data subjected to relatively high uncertainty or random noise, where it is also necessary to quantify the uncertainty of the measurement result. Both of these methods work with data of a similar nature, but each has certain specifics. The results obtained for IIT can thus serve as a reference for AFM. The project partners are the Czech Metrology Ins titute (CMI is the national metrology institute of the Czech Republic with top infrastructure in the field), the Institute of Measurement Science SAS (IMS SAS), and the Mathematical Institute SAS (MI SAS), which are academic institutions with extensive experience in basic research and applications of mathematics statistics in the field of measurement and metrology. This combination of partners brings a natural synergy and a combination of the necessary competencies for this |
| Investigation of biomedical effects of low frequency and pulsed electromagnetic fields | |
| Výskum biomedicínskych účinkov nízkofrekvenčných a pulzných elektromagnetických polí | |
| Program: | VEGA |
| Duration: | 1.1.2022 – 31.12.2024 |
| Project leader: | Mgr. Teplan Michal, PhD. |
| Annotation: | Although there is a persisting interest in both adverse and beneficial biological effects of electromagnetic fields(EMF), the unambiguous explanation of electromagnetic field influence on living structures is still lacking. For theimpact of low-frequency magnetic field (LF MF) experimental platform with monitoring of the cell growth curve based on impedance spectroscopy will test possible inhibition or stimulation dependent on the frequency and magnetic flux parameters. Effects of pulsed electric field (PEF) will be monitored by biological autoluminescence (BAL). Complexity measures will be utilized for ultrafast current recordings during the PEF application. For quantification of direct effects of PEF on microtubules (MT) and evaluation of kinesin molecule movement, advanced image processing methods will be developed. The relevance of this research area lies in theexploration of physical methods with possible contributions to diagnostics and therapy. |