Measurement electronics research group

Protecting underwater infrastructure and ensuring marine environmental safety in the Baltic Sea are crucial due to rising threats such as subsea cable damage, anchor incidents, and shipwreck-related pollution. This research integrates autonomous underwater sensor networks, advanced acoustic sensing, and artificial intelligence for enhanced maritime security and environmental monitoring. Acoustic arrays assess shipwreck conditions, including hull integrity, corrosion, and fuel presence, using advanced signal processing. Sensor nodes equipped with hydrophones continuously monitor underwater acoustic signals, detecting and classifying anthropogenic and natural sources. Machine learning-driven signal processing enables real-time risk assessment, anomaly detection, and communication with surface units. Expected outcomes include improved hazard detection, more efficient pollution monitoring, and autonomous decision-making, strengthening marine conservation and security in the Baltic Sea.

CitySense is an innovative urban sensing ecosystem that leverages a fleet of public vehicles to provide scalable, mobile data collection and advanced data analytics for smart cities. Unlike traditional static sensors, CitySense uses a modular, roaming hardware system that collects real-time data on urban infrastructure, air quality, and traffic conditions. The system aggregates data from existing sources and processes it using advanced AI models, offering actionable insights for city officials, businesses, researchers, and citizens. By transforming public vehicles into mobile sensing hubs, CitySense reduces the cost and complexity of city-wide sensor networks while ensuring comprehensive coverage of urban environments. The project focuses on addressing key urban challenges in Tallinn (Estonia), Pula (Croatia), and Dublin (Ireland), where each city faces unique issues such as road condition monitoring, traffic sign inventory, and air quality management. CitySense offers a flexible solution through its modular design, allowing the easy integration of various sensor modules to address specific city needs. This approach not only minimizes infrastructure costs but also improves data collection efficiency across large urban areas.

A platform for monitoring energy usage and operational state properties of distribution networks is elaborated, enabling users of distribution networks to access relevant and better time-resolution data to support green energy usage, energy efficiency and the flexibility technologies. The platform being developed will provide extensive support for decision-making and calculation algorithms, enabling efficient availability and management of local area operational data.