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  • Design and control of artificial spin ice 

    Strømberg, Anders (Doctoral theses at NTNU;2024:236, Doctoral thesis, 2024)
    Developing sustainable and robust data processing technology is an increasingly important global priority in an energy-scarce world where computational resources limit state-of-the-art applications. Power-hungry generative ...
  • Origin of Leakage Currents and Nanowire-to-Nanowire Inhomogeneity in Radial p-i-n Junction GaAs Nanowire Array Solar Cells on Si 

    Mukherjee, Anjan; Ren, Dingding; Mosberg, Aleksander Buseth; Vullum, Per Erik; Van Helvoort, Antonius; Fimland, Bjørn Ove Myking; Weman, Helge (Journal article; Peer reviewed, 2023)
    To realize the promising potential of radial junction nanowire (NW) array-based solar cells, it is crucial to get physical insight into how the overall photoconversion efficiency (PCE) is impacted by the hole mask properties, ...
  • Sensorimotor Synchronization in Embodied Rhythmic Agents 

    Darabi, Nima (Doctoral theses at NTNU;2024:150, Doctoral thesis, 2024)
    This thesis aims at modeling rhythmic collaboration to develop algorithms that can be implemented as an “embodied SMS agent,” an agent capable of coordinating rhythmically with its environment in a human way. This work ...
  • DNA walks in virus genomics 

    Belinsky, Alexandra; Kouzaev, Guennadi (Journal article; Peer reviewed, 2024)
    This paper studies published results in imaging and digital processing of virus RNAs (ribonucleic acid) using DNA (deoxyribonucleic acid) walks. The complicated nature and physicochemical properties of these nucleotide ...
  • Phased Array for UHF satellite ground station - Raising Efficiency and Lowering Costs through Heterodyne Steering 

    Marc Hölscher (Master thesis, 2024)
    Existing high gain antennas for the communication with orbiting satellites are often large to reach sufficient gain and require mechanical two-dimensional steering to be dynamically pointed at passing satellites (or other ...
  • Digital Significance 

    Sanchez Laws, Ana Luisa (Chapter, 2023)
    This chapter proposes the idea of ‘digital significance’ as a governance and decision- making process for assessing the value of digital collections. This concept is inspired by Australian approaches to valuing heritage, ...
  • A Multi-channel EEG Data Analysis for Poor Neuro-prognostication in Comatose Patients with Self and Cross-channel Attention Mechanism 

    Qadir, Hemin Ali; Nesaragi, Naimahmed; Halvorsen, Per Steinar; Balasingham, Ilangko (Chapter, 2023)
  • Exploring the Role of UX Influencing Factors in Virtual Reality for Natural Hazards Prepardness: A Disign-Based Approach 

    Irshad, Shafaq (Doctoral theses at NTNU;2024:152, Doctoral thesis, 2024)
    Climate change is one of the major challenges of our time, and its complexity makes finding innovative solutions challenging. One of the ways that climate change poses a challenge is by reshaping the earth’s natural ecosystem ...
  • Distributed Detection and Localization 

    Tabella, Gianluca (Doctoral theses at NTNU;2024:84, Doctoral thesis, 2024)
    This thesis delves into the detection and localization aspects of distributed Wireless Sensor Networks (WSNs). Specifically, the research concentrates on WSNs in which sensors autonomously carry out detection tasks and ...
  • NIRCA MkII DevKit Firmware Development 

    Øfsti, Gjermund Sollien (Master thesis, 2024)
    NIRCA MkII Development Board(NM2DB) er en PCB med NIRCA MkII(NM2), en ASIC brukt til kontroll og utlesning av infrarøde sensorer. PCBen har også en Trenz FPGA "system-på-modul" brukt til kontroll av ASICen, LDOer og for ...
  • Computationally-Efficient Structural Health Monitoring using Graph Signal Processing 

    Cheema, Muhammad Asaad; Sarwar, Muhammad Zohaib; Gogineni, Vinay Chakravarthi; Cantero Lauer, Daniel; Salvo Rossi, Pierluigi (Peer reviewed; Journal article, 2024)
    Structural health monitoring (SHM) of bridges is crucial for ensuring safety and long-term durability, however, standard damage-detection algorithms are computationally intensive. This article proposes a computationally ...
  • Optimization of Extracellular Vesicle Release for Targeted Drug Delivery 

    Damrath, Martin; Veletic, Mladen; Rudsari, Hamid Khoshfekr; Balasingham, Ilangko Sellappah (Journal article, 2023)
    Targeted drug delivery is a promising approach for many serious diseases, such as glioblastoma multiforme, one of the most common and devastating brain tumor. In this context, this work addresses the optimization of the ...
  • Classification of Kidney Tumor Grading on Preoperative Computed Tomography Scans 

    Mahootiha, Maryamalsadat; Qadir, Hemin Ali; Bergsland, Jacob; Balasingham, Ilangko Sellappah (Chapter, 2023)
    Deep learning (DL) has proven itself as a powerful tool to capture patterns that human eyes may not be able to perceive when looking at high-dimensional data such as radiological data (volumetric data). For example, the ...
  • Fish Monitoring in Aquaculture Using Multibeam Echosounders and Machine Learning 

    Kristmundsson, Johannus; Patursson, Oystein; Potter, John Robert; Xin, Qin (Peer reviewed; Journal article, 2023)
    Offshore salmon aquaculture is a growing industry that faces challenges such as sea lice infestations and varying environmental conditions, necessitating the development of new monitoring systems to improve fish welfare ...
  • Experimental assessment of a JANUS-based consensus protocol 

    Wengle, Johan Emil Hugo; Erstorp, Elias Strandell; Lidström, Viktor; Varagnolo, Damiano; Dong, Hefeng (Journal article; Peer reviewed, 2024)
    This paper proposes a distributed, JANUS-based protocol that enables an underwater acoustic network to reach consensus on arbitrary local opinions as numeric state variables. An envisioned scenario where nodes shall agree ...
  • Bayesian Fault Detection and Localization Through Wireless Sensor Networks in Industrial Plants 

    Tabella, Gianluca; Ciuonzo, Domenico; Paltrinieri, Nicola; Salvo Rossi, Pierluigi (Peer reviewed; Journal article, 2024)
    This work proposes a data fusion approach for quickest fault detection and localization within industrial plants via wireless sensor networks. Two approaches are proposed, each exploiting different network architectures. ...
  • On Enabling Scalable, Personalized, and Private Federated Learning 

    Gauthier, François (Doctoral theses at NTNU;2024:92, Doctoral thesis, 2024)
    This thesis investigates distributed machine learning for emerging Internet of Things (IoT) and Cyber-Physical Systems (CPS) applications. These applications involve large-scale data collection from distributed, often ...
  • Multi-Period Hybrid AC/DC-OPF Model for Flexibility Market Clearing With Seamless TSO-DSO Coordination 

    Sierazewski, Damian; Olsen, Ole Kjærland; Ivanko, Dmytro; Oleinikova, Irina; Farahmand, Hossein (Journal article; Peer reviewed, 2023)
    Unlocking flexibility assets from the consumer side and developing a well-functioning flexibility market (FM) is crucial to address the oncoming challenges in the EU power grid. In these conditions, the coordination between ...
  • Onboard Hyperspectral Classification Enables Georeferencing 

    Langer, Dennis David; Garrett, Joseph Landon; Birkeland, Roger; Berg, Simen; Orlandic, Milica (Journal article; Peer reviewed, 2023)
    The Norwegian University of Science and Technology (NTNU) has been building a system composed of multiple remote sensing agents for ocean observations. NTNU launched the first HYPerspectral Smallsat for Ocean observation ...
  • Multimodal deep learning for personalized renal cell carcinoma prognosis: Integrating CT imaging and clinical data 

    Mahootiha, Maryamalsadat; Qadir, Hemin Ali; Bergsland, Jacob; Balasingham, Ilangko (Peer reviewed; Journal article, 2023)
    Background and Objective: Renal cell carcinoma represents a significant global health challenge with a low survival rate. The aim of this research was to devise a comprehensive deep-learning model capable of predicting ...

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