Browsing Institutt for datateknologi og informatikk by Author "Akselsen, Sigmund"

Now showing items 1-6 of 6

    • Air Quality Prediction with Machine Learning 

      Lepperød, Andreas Jacobsen (Master thesis, 2019)
      I de senere år har luftkvalitet blitt et betydelig miljø- og helseproblem på grunn av rask urbanisering og industrialisering. På grunn av den påvirkning luftkvaliteten har på alles hverdag er nøyaktige observasjoner og ...

    • Blind Calibration of Air Quality Wireless Sensor Networks Using Deep Neural Networks 

      Veiga, Tiago Santos; Ljunggren, Erling; Bach, Kerstin; Akselsen, Sigmund (Chapter, 2021)
      Temporal drift of low-cost sensors is crucial for the applicability of wireless sensor networks (WSN) to measure highly local phenomenon such as air quality. The emergence of wireless sensor networks in locations without ...

    • Deep Learning for Blind Calibration of Wireless Sensor Networks 

      Ljunggren, Erling (Master thesis, 2020)
      Temporal drift of low-cost sensors is a crucial problem when considering the applicability of wireless sensor networks (WSN). Since they provide highly local measurements, which is key to combat the ever increasing problem ...

    • From a Low-Cost Air Quality Sensor Network to Decision Support Services: Steps towards Data Calibration and Service Development 

      Veiga, Tiago Santos; Munch-Ellingsen, Arne; Papastergiopoulos, Christoforos; Tzovaras, Dimitrios; Kalamaras, Ilias; Bach, Kerstin; Votis, Konstantinos; Akselsen, Sigmund (Peer reviewed; Journal article, 2021)
      Air pollution is a widespread problem due to its impact on both humans and the environment. Providing decision makers with artificial intelligence based solutions requires to monitor the ambient air quality accurately and ...

    • On the Use of Air Quality Microsensors for Supporting Decision Makers 

      Bach, Kerstin; Akselsen, Sigmund; Veiga, Tiago Santos; Kalamaras, Ilias (Chapter, 2020)
      In this poster we present how a network of Internet-of-things (IoT) devices facilitated through machine learning can improve decision making. Our application domain is air quality in the municipality of Trondheim. Ambient ...

    • Visual analytics for exploring air quality data in an AI-enhanced IoT environment 

      Kalamaras, Ilias; Xygonakis, Ioannis; Glykos, Konstantinos; Akselsen, Sigmund; Munch-Ellingsen, Arne; Nguyen, Hai Thanh; Jacobsen Lepperod, Andreas; Bach, Kerstin; Votis, Konstantinos; Tzovaras, Dimitrios (Chapter, 2019)
      Visual analytics have an important role in the exploration and analysis of large amounts of data in IoT applications. Data visualizations can provide overviews of different aspects of data and user interaction can assist ...