Projects

A reconfigurable robot system is being developed to check the quality and correct function of machine vision IoT modules. Variations of IoT modules and different are different visual quality control reliability is 80-87%. To make and fix the products, we have designed and manufactured a multi-position special jig, which is controlled by a robot. In order to identify products, a database of IoT modules was created, with the help of which algorithms identify and select the product. Also, quality assessment works based on the same logic. Checked products are divided into two: OK and defective. Defective products are separated and determined to be the actual defect.

The objective of the EEV5040 Industrial Automation and Drives activity is to introduce students to the importance of industrial automation and electric drives and the latest trends in these fields (including IoT). As a result of the development activities, students will acquire in-depth knowledge of electric drive management, model-based design methodology and IoT applications in industrial automation in the future. They are able to create, adapt and analyse electric motor control systems and solve real problems in this field. These results influence the quality of industry-specific ICT teaching, providing students with the practical skills and knowledge essential to today's industrial automation. Within the framework of the development project, the subject EEM0040 Machine vision is also amended, where the traditional machine vision curriculum is added to it by adding the hyperspectral technology component. The aim of the development activity is to combine concepts of machine vision with hyperspectral data processing. Within the subject, students will develop practical skills in the use of hyperspectral cameras, from camera setup to processing of various hyperspectral images. They acquire knowledge of the specifics of hyperspectral data, such as the wavelength spectrum and its relationship to the properties of materials. In addition, they learn the methods of machine vision, which allow to identify different objects and characteristics from hyperspectral images.

The objective of this collaborative project is to enable trustworthy AI hardware by explainable and efficient Deep Neural Networks. As the main contribution to achieving this objective, the project will establish an EnTrustED Framework for DNN hardware design analysis that will follow the novel design flow. First, at the design-time phase, a combination of DNN-tailored AxC techniques will be provided to enhance the compute-efficiency of the DNN inference hardware. The Framework will enable a simulation-based analysis for identifying the neurons that are not practical for the optimisation and must keep their initial Exact Computing (ExC) implementation or the approximation should be reduced. It aims to equip the AI hardware with self-test mechanisms to detect hardware errors and fault-tolerance mechanisms for recovering from an error that has occurred and, thereby, continue the AI algorithm uninterruptedly. As one of the novelties, this project views eXplainable AI (XAI) from a hardware perspective. We intend to consider the AxC during the explainability and thus ensure a correct explanation of the decision taken by the DNN. Once we guarantee the correct behaviour of the hardware, and we have properly considered AxC, we can safely run explainability approaches to profile the DNN implantation still at the design-time for identification of DNN input specific significant neurons. The experimental nature of the project and the high interdependency of the contributions by EC-Lyon and TalTech make the envisioned face-to-face visits essential to achieving the goals of the collaboration.

With a current market value of 150 billion EUR/year, fish are among the most economically valuable species on the planet. The implementation of the European Water Framework and Habitats Directives ensure that long-term environmental monitoring is in demand across the EU. Economically valuable fish species include Salmon and critically endangered European Eel (native in Estonia). They must migrate from the sea into rivers as part of their lifecycle. Existing solutions for fish monitoring are expensive, requiring manual processing of 1000s of hours of video for each location. The "ExoFish" project will provide a highly needed and valuable commercial system capable of automatically detecting and counting wild fish with significantly better performance at 50% of the cost of existing technologies, by utilizing custom hardware and underwater artificial intelligence methods developed at TalTech in collaboration with the Estonian Environmental Agency and international commercial partners.

Field study of risk of fish injury and mortality at two large hydropower plants in Norway and two in Sweden.

The WaveTwin project aims to further develop a digital twin for estimating the wave state in the Baltic Sea from Synthetic Aperture Radar (SAR) data. Previous methods for SAR data are not successful enough for enclosed water bodies where steep wind waves are dominating. It is apparent that the Baltic Sea also cannot be covered fully with wave buoys to measure the energy spectrum, i.e. the distribution of wave energy by frequency. The use of SAR data allows us to effectively evaluate the wave spectrum over the entire Baltic Sea, skipping the high costs of installing and operating wave buoys. To achieve this goal, the team will use their expertise in the application of deep learning techniques to estimate wave spectra based on SAR images. Environmental protection and navigation at sea (e.g. for the construction and maintenance of wind farms) are two of the most important application areas. A growing interest in such problems is also evident in the field of situational awareness systems.

The purpose of the project is to demonstrate the usefulness and added value of the data layers being created and thereby strengthen the capacity of Estonian industry in the use of EO data. The research and development activities of the project are aimed at: (1) artificial intelligence-based software development for estimating wave parameters in the coastal zone and (2) optimization of InSAR processing capabilities.

The program supports the development of green skills necessary for the food processing companies. In the course of the activity, the updating of the learning content related to the teaching of green skills in higher and vocational education, further and retraining for the development of green skills in order to prepare a workforce with broader knowledge and skills regarding the green economy for the introduction of new technologies and approaches, as well as the updating of professional standards and skill profiles or, if necessary, the creation of new ones, and the further training of vocational teachers-lecturers are supported.

BACHA project aims to remind European citizens about an historical event, as an example of Europeanism and intensive work for a better society.

The BIOCONNECT project aims to help create a more robust, integrated, and inclusive Baltic ecosystem/cluster for biotechnology innovation as one of the key ingredients for the present and future economic competitiveness of the region. A strong and vibrant bioeconomy holds colossal potential to reinforce EU’s focus on sustainability and circularity, ensure a healthier, more active population - as well as pave the way for an exponential cascade of new applications so transformative as to be dubbed the Bio Revolution.

The development project of an artificial intelligence-based management system Chemi-Pharm focuses on organizing and visualizing the company's daily production logistics through a smart artificial intelligence-based management system, which contributes to increasing the production throughput and efficiency of production processes.

Emerging education in the fields of electrical engineering, informatics engineering, ICT and cybersecurity is crucial for addressing the challenges of the Green Deal, initiated by the European Commission. The development of new technologies and systems for transitioning to a green industry requires the preparation of the next generation of professionals and leaders with the knowledge, skills, and values needed to address the complex challenges of sustainable development. The aim of this project is to bring sustainability-focused insights into the study programs of the partner institutions for the sharing of experience, knowledge, best teaching-oriented technology-driven education practices, and achievements in electrical engineering and automation, mechatronics, adaptronics, information and communication technologies, informatics engineering, and cybersecurity that correspond to the needs of the green industry. Within this project, the growth of competencies aimed at meeting the demands of the industry in the Baltic and Nordic regions is expected.