Principal Investigator: Martha Arbayani Zaidan
Funder: Academy of Finland Research Fellowship
Budget: € 937,803 (€ 656,464 funded by the Academy of Finland + € 281,339 funded by the University of Helsinki)
Project duration: 01/09/2023 → 31/08/2027
This project will develop and implement Artificial Intelligence (AI) technology to improve the quality of data generated at environmental research stations. The project development and evaluation will be carried out mainly at the Station for Measuring Ecosystem-Atmosphere Relations (SMEAR stations) network nationally and internationally. The outcome of the project will lead to the automation of the SMEAR infrastructure, where scientist involvement in performing repetitive tasks can be minimized and data quality improved. This will be achieved by AI automation of data quality control and instrument calibration; these time and error-prone tasks are currently performed manually by scientists. The AI implementation will be beneficial on a global scale as the number of research stations grows across the world.
Collaborators: CSC IT for Sciences (Finland), Finnish Meteorological Institute (FMI, Finland), Helsinki Region Environmental Services (HSY, Finland), Purdue University (US), Cyprus Institute (Cyprus), TU Delft (Netherlands), University of Eastern Finland (Kuopio, Finland) and Nanjing University (China).
Principal Investigators: Tuukka Petäjä, Tareq Hussein, Martha Arbayani Zaidan
Funder: European Commission Joint Research Centre - European Partnership on Metrology
Budget: € 110,000 (total funding of € 2,589,828)
Project duration: 01/09/2023 → 31/08/2026
Sensor networks are used in a large number of fields but are struggling with data quality of varying degrees, with unknown measurement uncertainty and lack of traceability to the SI limiting their applicability. To overcome these issues, this project will address the metrological aspects of sensor networks, covering the uncertainty propagation, data quality metrics and SI-traceability in generic sensor networks, as well as the assessment, infrastructure, and risk analysis of distributed sensor networks alongside software frameworks and semantics via automated application of developed methods. The applicability of the methods, tools, and concepts will be demonstrated in typical real-world sensor networks.
Collaborators: VTT (Finland), CMI (Czech Republic), DTI (Denmark), FORCE Technology (Denmark), IPQ (Portugal), LNE (France), METROSERT (Estonia), PTB (Germany), VSL (Netherlands), Airparif (Paris, France), FZ-JUELICH (Germany), NPL (UK), University of Cambridge (UK), VINS (Serbia), CCPI (UK), Random Red (Croatia), Vaisala (Finland), METAS (Switzerland).
Principal Investigators: Martha Arbayani Zaidan, Tuomo Nieminen, Sasu Tarkoma
Funder: Helsinki Institute for Information Technology (HIIT)
Budget: ~ € 10,000
Project duration: 01/09/2023 → 30/11/2023
Principal Investigators: Martha Arbayani Zaidan, Jonathan Atherton, Jaana Back, Sasu Tarkoma
Funder: Helsinki Institute for Information Technology (HIIT) and Academy of Finland
Budget: ~ € 20,000
Project duration: 01/09/2023 → 29/02/2024