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IECS

Institute of Electronics and Computer Science
Country: Latvia
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26 Projects, page 1 of 6
  • Project . 2022 - 2025
    Open Access mandate for Publications and Research data
    Funder: EC Project Code: 101070660
    Overall Budget: 1,993,340 EURFunder Contribution: 1,993,340 EUR
    Partners: PRES, FREE SILICON FOUNDATION (I), CSIC, FIBRASERVI BVBA, IECS

    Europe's IT hardware development is constantly challenged by outrageously expensive development tools, legal constraints like NDAs or patents, lock-in threats, dependency from external vendors or supply chains and foreign political events. Europe’s digital infrastructure (from consumer to critical appliances) is heavily relying on foreign closed-source chips which are literally black-boxes which may (and have been proven to) contain malicious features. This situation makes the hardware development expensive and inefficient, and undermines the very principle of sovereignty, resilience and re-usability. Open-source silicon chips, which are open in their entirety, i.e. down to the physical layout, carry the potential of catapulting Europe into a renaissance of digital technology. Several challenges are on the way, many of which will require the participation of the stakeholders (from the fertile ground made of “nerdy” hobbyists and makers who are the early protagonists of the scene, all the way up to large enterprises), as well as the participation of policymakers and regulatory bodies. The road ahead is steep, but rich of rewards. Therefore we loudly say: Go IT!

  • Funder: CHIST-ERA Project Code: CHIST-ERA-20-BCI-004
    Partners: BGU, Laboratory of Intelligent Interfaces of Information and Communication Systems, Technical University Kosice, ETH Zurich, Sensomedical labs ltd., TUL, IECS, Institute of Measurement Science, Slovak Academy od Sciences

    Motivation for the study: 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-behavioural 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. Project objective: We propose 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. Implementation: Careful attention will be paid to balance known neurophysiological evidence of the process with artificial intelligence (AI) within the active BCI protocols to avoid running into conceptual pitfalls. Computed features of EEG signals will serve to monitor the patient's engagement, cognitive workload, or mental fatigue in real-time. These indicators will be combined with observable patient’s performance and behaviours to improve the accuracy of mental state estimation. Exceeding critical mental state levels will signal the therapist to activate appropriate countermeasures in the form of environmental and task changes. Research and technological challenges: To challenge and overcome existing technologies, commercially available head-mounted VR displays (HMD) combined with miniaturized energyefficient microcontroller units will be employed for EEG signal processing, BCI discrimination and on-board classification implementation, and a full-duplex communication with the HMD controllers. Advanced dry EEG sensors suitable to operate and be placed on the scalp without interfering with the HMD will be developed and tested. A novel patient-to-therapist multimodal collaborative environment augmented through VR immersion and by AI monitored patient’s brain activity will be created. By combining these pieces, a low-power wearable BCI-HMD system will be constructed. A series of clinical studies will validate the system.

  • Funder: EC Project Code: 262212
    Partners: IECS, AP2K, SMART Group, CIT, TWI LIMITED, Semicon, CERTH, INNOSPEXION APS
  • Open Access mandate for Publications and Research data
    Funder: EC Project Code: 101095672
    Overall Budget: 5,808,740 EURFunder Contribution: 5,808,740 EUR
    Partners: FIF, EURONET CONSULTING, LU, IECS, CHECKHEALTH SWEDEN, HK3 LAB S.R.L., CNR, Medical University of Graz, SCUOLA DI ROBOTICA, SPINDOX LABS SRL

    The incidence of undiagnosed diabetes accounts for 36% European adults, while 541M adults worldwide have Impaired Glucose Tolerance (IGT), an important risk factor for further T2D development. Both IGT and/or Impaired Fasting Glucose (IFG) are intermediate glucose mishandling (i.e. intermediate conditions in the healthy-T2D transition) and are manifestations of the so-called prediabetes condition. Prediabetes itself is not an extensively studied condition compared to the overt T2D, but it is also a condition that can be reversed without the prescription usage to not proceed into T2D. The aim of our project is to develop a prototype tool for the real-time prediction of the prediabetic risk based on a series of patient-specific mathematical models (firstly developed during the FP7 MISSION-T2D project) that simulate metabolism, pancreas hormone production, microbiome metabolites, inflammatory process and immune system response. The prediction algorithm will be based on a “physics-informed machine learning” approach. A rich dataset of real-life data will be combined with a mathematical model to overcome the limits of a “black-box” ML approach, while reducing the computational time for simulating the solutions of a heavy mathematical models and improving its prediction performances.We will collect the necessary training data (e.g., diet questionnaire, physical activity, blood metabolites and microbiome) from already existing clinical studies (used as retrospective trials) which are representative of the real-life scenarios of a prediabetes/diabetes risk insurgence in adulthood (20-80y): family history, Metabolic Syndrome, Liver disease and obesity. A newly dedicated multicentric pilot prospective observational study will be also performed, during which we will also equip the participants with wearable sensors (e.g. glucose monitoring, bioimpedance, heart rate, accelerometer).

  • Open Access mandate for Publications and Research data
    Funder: EC Project Code: 101092161
    Overall Budget: 5,455,000 EURFunder Contribution: 4,400,800 EUR
    Partners: DIGIOTOUCH OU, ROMANIAN STANDARDS ASSOCIATIONASRO, DORMAKABA DEUTSCHLAND GMBH, AMTP, CEMOSA, CETMA, IECS, E-METODI- S.R.L., INSTITUTE FOR APPLIED BUILDING INFORMATICS IABI, in2it...

    openDBL intends to integrate multidisciplinary know-how to cover the requirements of the Call and solve the issues of the current situation. The challenge of the project is to allow, through the development of openAPI, the disposal of openDBL in a unique standardized platform and create useful content, to simplify the workload of the AECO industry. The project pursues 3 objectives: 1) create a DBL with useful content and functionalities, 2) ensure openDBL is usable and simple to use, reducing the time spent to upload, search and process the information and data to facilitate usage and gain wide adoption, 3) ensure attractive economics, through value propositions and convenient pricing. We’ll provide any user with an integrated platform for their digitization needs; ensure that information and data conform to the latest trends and needs of our target clients and support the EU's circular economy and green policies; develop automatic classification systems and data standards; facilitate the operation and maintenance activities of the buildings. This will be achieved creating an Information Delivery Manual and a Data Model and further developing our existing platform used to create a DBL for an important Italian Public Contracting Authority. openDBL will support data matching with external databases and will integrate state of-the art technologies (AI, Blockchain, IoT and VR). Our ambition is to make openDBL the platform of reference for the monitoring of building consumption, transparencies of transactions and official documents, and the positive impact on maintenance and environment.