Blar i NTNU Open på forfatter "Murad, Abdulmajid"

Viser treff 1-5 av 5

    • Information-Driven Adaptive Sensing Based on Deep Reinforcement Learning 

      Murad, Abdulmajid; Kraemer, Frank Alexander; Bach, Kerstin; Taylor, Gavin (Chapter, 2020)
      In order to make better use of deep reinforcement learning in the creation of sensing policies for resource-constrained IoT devices, we present and study a novel reward function based on the Fisher information value. This ...

    • Probabilistic Deep Learning to Quantify Uncertainty in Air Quality Forecasting 

      Murad, Abdulmajid; Kraemer, Frank Alexander; Bach, Kerstin; Taylor, Gavin (Peer reviewed; Journal article, 2021)
      Data-driven forecasts of air quality have recently achieved more accurate short-term predictions. However, despite their success, most of the current data-driven solutions lack proper quantifications of model uncertainty ...

    • Sampling rate comparison in accelerometer based human activity recognition 

      Castelló Garcia, Daniel (Master thesis, 2019)
      Human activity recognition aims to identify patterns in data generated through human activity. This activity commonly describes movement and can be gathered through a plethora of sensors. Given their low price and ...

    • Uncertainty-Aware Autonomous Sensing with Deep Reinforcement Learnings 

      Murad, Abdulmajid (Doctoral theses at NTNU;2023:64, Doctoral thesis, 2023)
      The goal of many Internet of Things (IoT) sensing applications, such as environmental monitoring, is to support decision-making by providing valuable information about various phenomena. One approach to achieve this goal ...

    • Using Neural Networks for IoT Power Management 

      Stephansen-Smith, Finn Julius (Master thesis, 2020)
      De fleste enheter i Tingenes Internett (IoT) har begrenset batterilevetid. For å likevel kunne være pålitelige er de nødt til å utnytte batteriet på en så optimal måte som mulig. Dette prosjektet ser på hvorvidt nevrale ...