Viser treff 241-260 av 5334

    • Consent Management System on Patient-Generated Health Data 

      Randine, Pietro; Salant, Eliot; Muzny, Miroslav; Pape-Haugaard, Louise (Chapter; Bokkapittel, 2024)
      We consent to many things in life, but sometimes we do not know what we consent to. When discussing data protection in Europe, consent has been associated with permission under the GDPR, and health data are highly sensitive. Patients cannot make an informed decision without being provided with the information they need upfront: no informed decision, no informed consent. This paper presents a consent ...
    • AI-Based Cropping of Soccer Videos for Different Social Media Representations 

      Houshmand Sarkhoosh, Mehdi; Dorcheh, Sayed Mohammad Majidi; Midoglu, Cise; Sabet, Saeed; Kupka, Tomas; Johansen, Dag; Riegler, Michael Alexander; Halvorsen, Pål (Chapter; Bokkapittel, 2024-01-29)
      The process of re-publishing soccer videos on social media often involves labor-intensive and tedious manual adjustments, particularly when altering aspect ratios while trying to maintain key visual elements. To address this issue, we have developed an AI-based automated cropping tool called SmartCrop which uses object detection, scene detection, outlier detection, and interpolation. This innovative ...
    • SmartCrop-H: AI-Based Cropping of Ice Hockey Videos 

      Dorcheh, Sayed Mohammad Majidi; Houshmand Sarkhoosh, Mehdi; Midoglu, Cise; Sabet, Saeed; Kupka, Tomas; Johansen, Dag; Halvorsen, Pål (Chapter; Bokkapittel, 2024-04-17)
      Sports multimedia plays a central role in captivating audiences on social media platforms. However, fast-paced sports such as ice hockey pose unique challenges due to their swift gameplay and the small puck size, making object tracking-based video adaptation for social media a complex task. In this context, we introduce SmartCrop-H, an innovative ice hockey video cropping tool powered by advanced ...
    • Representing Power Variability of an Idle IoT Edge Node in the Power State Model 

      Tofaily, Salma; Rais, Issam; Anshus, Otto Johan (Chapter; Bokkapittel, 2023-05-01)
      Simulations can be used to efficiently predict and explore energy consumption of nodes in cyber-physical and IoT systems. The Power State Model (PSM), widely used in simulators, uses only a single value for the energy consumption, for each power state of a node. However, for a given state (including the idle state) the actual consumed energy can vary. Consequently, PSM having a single value only per ...
    • Design and Evaluation of Single-Board Computer Based Power Monitoring for IoT and Edge Systems 

      Guégan, Loïc; Tofaily, Salma; Rais, Issam (Chapter; Bokkapittel, 2023-05-01)
      Internet of Things (IoT) and edge platforms are very complex systems. They are heterogeneous in terms of hardware and software. In these systems, being able to document the energy consumed by the nodes is important. To mitigate the impact of such systems on the energy consumption and improve their energy efficiency, research experiments involving power monitoring tools are required.However, in the ...
    • Compact representation for memory-efficient storage of images using genetic algorithm-guided key pixel selection 

      Malakar, Samir; Banerjee, Nirwan; Prasad, Dilip Kumar (Journal article; Tidsskriftartikkel; Peer reviewed, 2024-11-04)
      In the past few years, we have observed rapid growth in digital content. Even in the biological domain, the arrival of microscopic and nanoscopic images and videos captured for biological investigations increases the need for space to store them. Hence, storing these data in a storage-efficient manner is a pressing need. In this work, we have introduced a compact image representation technique with ...
    • Hyperspectral imaging and deep learning for parasite detection in white fish under industrial conditions 

      Syed, Shaheen; Ortega, Samuel; Anderssen, Kathryn Elizabeth; Nilsen, Heidi; Heia, Karsten (Journal article; Tidsskriftartikkel; Peer reviewed, 2024-11-09)
      Parasites in fish muscle present a significant problem for the seafood industry in terms of both quality and health and safety, but the low contrast between parasites and fish tissue makes them exceedingly difficult to detect. The traditional method to identify nematodes requires removing fillets from the production line for manual inspection on candling tables. This technique is slow, labor intensive ...
    • Optimization of trigonometric polynomials with crystallographic symmetry and spectral bounds for set avoiding graphs 

      Hubert, Evelyne; Metzlaff, Tobias; Moustrou, Philippe; Riener, Cordian Benedikt (Journal article; Tidsskriftartikkel; Peer reviewed, 2024-11-05)
      We provide a new approach to the optimization of trigonometric polynomials with crystallographic symmetry. This approach widens the bridge between trigonometric and polynomial optimization. The trigonometric polynomials considered are supported on weight lattices associated to crystallographic root systems and are assumed invariant under the associated reflection group. On one hand the invariance ...
    • AI-Based Cropping of Ice Hockey Videos for Different Social Media Representations 

      Houshmand Sarkhoosh, Mehdi; Dorcheh, Sayed Mohammad Majidi; Midoglu, Cise; Sabet, Saeed Shafiei; Kupka, Tomas; Johansen, Dag; Riegler, Michael Alexander; Halvorsen, Pål (Journal article; Tidsskriftartikkel; Peer reviewed, 2024-08-23)
      Sports multimedia is among the most prominent types of content distributed across social media today, and the retargeting of videos for diverse aspect ratios is essential for a suitable representation on different social media platforms. In this respect, ice hockey is quite challenging due to its agile movement pattern and speed, and because the main reference point (puck) is very small. In this ...
    • Validating polyp and instrument segmentation methods in colonoscopy through Medico 2020 and MedAI 2021 Challenges 

      Jha, Debesh; Sharma, Vanshali; Banik, Debapriya; Bhattacharya, Debayan; Roy, Kaushiki; Hicks, Steven; Tomar, Nikhil Kumar; Thambawita, Vajira L B; Krenzer, Adrian; Ji, Ge-Peng; Poudel, Sahadev; Batchkala, George; Alam, Saruar; Ahmed, Awadelrahman M.A.; Trinh, Quoc-Huy; Khan, Zeshan; Nguyen, Tien-Phat; Shrestha, Shruti; Nathan, Sabari; Gwak, Jeonghwan Gwak; Jha, Ritika Kumari; Zhang, Zheyuan; Schlaefer, Alexander; Bhattacharjee, Debotosh; Bhuyan, M.K.; Das, Pradip K.; Fan, Deng-Ping; Parasa, Sravanthi; Ali, Sharib; Riegler, Michael Alexander; Halvorsen, Pål; de Lange, Thomas; Bagci, Ulas (Journal article; Tidsskriftartikkel; Peer reviewed, 2025-09-05)
      Automatic analysis of colonoscopy images has been an active field of research motivated by the importance of early detection of precancerous polyps. However, detecting polyps during the live examination can be challenging due to various factors such as variation of skills and experience among the endoscopists, lack of attentiveness, and fatigue leading to a high polyp miss-rate. Therefore, there is ...
    • A Theoretical and Empirical Analysis of 2D and 3D Virtual Environments in Training for Child Interview Skills 

      Salehi, Pegah; Hassan, Syes Zohaib; Baugerud, Gunn Astrid; Powell, Martine; Sinkerud Johnson, Miriam; Johansen, Dag; Sabet, Saeed; Riegler, Michael Alexander; Halvorsen, Pål (Journal article; Tidsskriftartikkel; Peer reviewed, 2024-08-12)
      This paper presents a detailed study of an AI-driven platform designed for the training of child welfare and law enforcement professionals in conducting investigative interviews with maltreated children. It achieves a subjective simulation of interview situation through the integration of fine-tuned GPT-3 models within the Unity framework. The study recruited participants from a range of backgrounds, ...
    • Machine learning based prognostics and statistical optimization of the performance of biogas-biodiesel blends powered engine 

      Paramasivam, Prabhu; Alruqi, Mansoor; Dhanasekaran, Seshathiri; Albalawi, Fahad; Hanafi, H.A.; Saad, Waleed (Journal article; Tidsskriftartikkel; Peer reviewed, 2024-09-12)
      In this study, waste biomass-derived biogas was employed as the main fuel while the biodiesel-diesel blend was used as pilot fuel. This paper describes the development of a Decision Tree and Response Surface methodology-based statistical framework for prediction modeling and optimization. The compression ratio, fuel injection time, fuel injection pressure, and biogas flow rate were employed as ...
    • Sea ice mass balance during the MOSAiC drift experiment: Results from manual ice and snow thickness gauges 

      Raphael, Ian A.; Perovich, Donald K.; Polashenski, Christopher M.; Clemens-Sewall, David; Itkin, Polona; Lei, Ruibo; Nicolaus, Marcel; Regnery, Julia; Smith, Madison M.; Webster, Melinda; Jaggi, Matthias (Journal article; Tidsskriftartikkel; Peer reviewed, 2024-07-09)
      Precise measurements of Arctic sea ice mass balance are necessary to understand the rapidly changing sea ice cover and its representation in climate models. During the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition, we made repeat point measurements of snow and ice thickness on primarily level first- and second-year ice (FYI, SYI) using ablation stakes and ...
    • Leveraging explainable artificial intelligence for early prediction of bloodstream infections using historical electronic health records 

      Bopche, Rajeev; Nytrø, Øystein; Gustad, Lise Tuset; Afset, Jan Egil; Damås, Jan Kristian; Ehrnström, Birgitta (Journal article; Tidsskriftartikkel; Peer reviewed, 2024-11-14)
      Bloodstream infections (BSIs) are a severe public health threat due to their rapid progression into critical conditions like sepsis. This study presents a novel eXplainable Artificial Intelligence (XAI) framework to predict BSIs using historical electronic health records (EHRs). Leveraging a dataset from St. Olavs Hospital in Trondheim, Norway, encompassing 35,591 patients, the framework integrates ...
    • Enhancing Medical Image Quality Using Fractional Order Denoising Integrated with Transfer Learning 

      Annadurai, Abirami; Sureshkumar, Vidhushavarshini; Jaganathan, Dhayanithi; Dhanasekaran, Seshathiri (Journal article; Tidsskriftartikkel; Peer reviewed, 2024-08-29)
      In medical imaging, noise can significantly obscure critical details, complicating diagnosis and treatment. Traditional denoising techniques often struggle to maintain a balance between noise reduction and detail preservation. To address this challenge, we propose an “Efficient Transfer-Learning-Based Fractional Order Image Denoising Approach in Medical Image Analysis (ETLFOD)” method. Our approach ...
    • Point-cloud clustering and tracking algorithm for radar interferometry 

      Ivarsen, Magnus Fagernes; St‐Maurice, Jean-Pierre; Hussey, Glenn C.; Huyghebaert, Devin Ray; Gillies, D. Megan (Journal article; Tidsskriftartikkel; Peer reviewed, 2024-10-22)
      In data mining, density-based clustering, which entails classifying datapoints according to their distributions in some space, is an essential method to extract information from large datasets. With the advent of software-based radio, ionospheric radars are capable of producing unprecedentedly large datasets of plasma turbulence backscatter observations, and new automatic techniques are needed to ...
    • The consequences of tritium mix for simulated ion cyclotron emission spectra from deuterium-tritium plasmas 

      Slade-Harajda, T.W.; Chapman, Sandra; Dendy, R.O. (Journal article; Tidsskriftartikkel; Peer reviewed, 2024-10-16)
      Measurements of ion cyclotron emission (ICE) are obtained from most large magnetically confined fusion plasma experiments, and may be used in future to quantify properties of the fusion-born alpha-particle population in deuterium-tritium (DT) plasmas in ITER. ICE is driven by spatially localised, strongly non-Maxwellian, minority energetic ion populations which relax collectively under the ...
    • Polar mesospheric summer echo (PMSE) multilayer properties during the solar maximum and solar minimum 

      Jozwicki, Dorota; Sharma, Puneet; Huyghebaert, Devin Ray; Mann, Ingrid Brigitte (Journal article; Tidsskriftartikkel; Peer reviewed, 2024-11-11)
      Polar mesospheric summer echoes (PMSEs) are radar echoes that are measured in the upper atmosphere during the summer months and that can occur in several layers. In this study, we aimed to investigate the relationship between PMSE layers ranging from 80 to 90 km altitude and the solar cycle. We investigated 230 h of observations from the EISCAT very high frequency (VHF) radar located near Tromsø, ...
    • Intermittent fluctuations at the boundary of magnetically confined plasmas 

      Losada, Juan Manuel (Doctoral thesis; Doktorgradsavhandling, 2024-11-29)
      This thesis presents a comprehensive analysis of a stochastic model for transport due to the motion of uncorrelated localized structures. The motivation for this model is found in the scrape-off layer (SOL) of magnetically confined plasmas, but its applicability extends to other turbulent or chaotic systems. This model provides a statistical framework to explore the implications of different transport ...
    • Liftable Point-Line Configurations: Defining Equations and Irreducibility of Associated Matroid and Circuit Varieties 

      Clarke, Oliver; Masiero, Giacomo; Mohammadi, Fatemeh (Journal article; Tidsskriftartikkel; Peer reviewed, 2024-09-28)
      We study point-line configurations through the lens of projective geometry and matroid theory. Our focus is on their realization spaces, where we introduce the concepts of liftable and quasi-liftable configurations, exploring cases in which an n-tuple of collinear points can be lifted to a nondegenerate realization of a point-line configuration. We show that forest configurations are liftable ...