Recent additions

  • Lithium-ion battery digitalization: Combining physics-based models and machine learning 

    Amiri, Mahshid N.; Håkansson, Anne Eva Margareta; Burheim, Odne Stokke; Lamb, Jacob Joseph (Journal article; Tidsskriftartikkel; Peer reviewed, 2024-05-21)
    Digitalization of lithium-ion batteries can significantly advance the performance improvement of lithium-ion batteries by enabling smarter controlling strategies during operation and reducing risk and expenses in the design and development phase. Accurate physics-based models play a crucial role in the digitalization of lithium-ion batteries by providing an in-depth understanding of the system. ...
  • Guided U-Net Aided Efficient Image Data Storing with Shape Preservation 

    Banerjee, Nirwan; Malakar, Samir; Gupta, Deepak Kumar; Horsch, Ludwig Alexander; Prasad, Dilip Kumar (Chapter; Bokkapittel, 2023-11-02)
    The proliferation of high-content microscopes ( 32 GB for a single image) and the increasing amount of image data generated daily have created a pressing need for compact storage solutions. Not only is the storage of such massive image data cumbersome, but it also requires a significant amount of storage and data bandwidth for transmission. To address this issue, we present a novel deep learning ...
  • Deidentifying a Norwegian clinical corpus - An effort to create a privacy-preserving Norwegian large clinical language model 

    Ngo, Phuong Dinh; Tejedor Hernandez, Miguel Angel; Olsen Svenning, Therese; Chomutare, Taridzo Fred; Budrionis, Andrius; Dalianis, Hercules (Journal article; Tidsskriftartikkel; Peer reviewed, 2024)
    This study discusses the methods and challenges of deidentifying and pseudonymizing Norwegian clinical text for research purposes. The results of the NorDeid tool for deidentification and pseudonymization on different types of protected health information were evaluated and discussed, as well as the extension of its functionality with regular expressions to identify specific types of sensitive ...
  • Real-Time Change Detection with Convolutional Density Approximation 

    Ha, Synh Viet-Uyen; Nguyen, Tien Cuong; Phan, Hung Ngoc; Ha, Hoai Phuong (Journal article; Tidsskriftartikkel; Peer reviewed, 2024-04-02)
    Background Subtraction (BgS) is a widely researched technique to develop online Change Detection algorithms for static video cameras. Many BgS methods have employed the unsupervised, adaptive approach of Gaussian Mixture Model (GMM) to produce decent backgrounds, but they lack proper consideration of scene semantics to produce better foregrounds. On the other hand, with considerable computational ...
  • Augmented Reality enhanced device usage training tool for in-home health-self-monitoring by pregnant women 

    Ghimire Subedi, Sarala; Martinez, Santiago; Hartvigsen, Gunnar; Gerdes, Martin (Journal article; Tidsskriftartikkel, 2023-09)
    Virtual care comprising virtual visits and monitoring via audio or video has the potential to reduce access barriers to care and has been successfully implemented in prenatal care. It reduces the frequency of in-person visits and increases self-care skills. However, the knowledge and competence in handling monitoring equipment at home directly influences satisfaction and engagement with the ...
  • Mabnet: Master Assistant Buddy Network With Hybrid Learning for Image Retrieval 

    Agarwal, Rohit; Das, Gyanendra; Aggarwal, Saksham; Horsch, Ludwig Alexander; Prasad, Dilip Kumar (Journal article; Tidsskriftartikkel; Peer reviewed, 2023-05-05)
    Image retrieval has garnered a growing interest in recent times. The current approaches are either supervised or self-supervised. These methods do not exploit the benefits of hybrid learning using both supervision and self-supervision. We present a novel Master Assistant Buddy Network (MAB-Net) for image retrieval which incorporates both the learning mechanisms. MABNet consists of master and assistant ...
  • Aux-Drop: Handling Haphazard Inputs in Online Learning Using Auxiliary Dropouts 

    Agarwal, Rohit; Prasad, Dilip Kumar; Horsch, Ludwig Alexander; Gupta, Deepak Kumar (Journal article; Tidsskriftartikkel; Peer reviewed, 2023)
    Many real-world applications based on online learning produce streaming data that is haphazard in nature, i.e., contains missing features, features becoming obsolete in time, the appearance of new features at later points in time and a lack of clarity on the total number of input features. These challenges make it hard to build a learnable system for such applications, and almost no work exists in ...
  • Weather-aware Wake-up of Sleeping Cyber-Physical IoT Nodes 

    Kristensen, Steffen Ole Randrup; Bjørndalen, John Markus; Rais, Issam; Ha, Hoai Phuong; Anshus, Otto Johan (Chapter; Bokkapittel, 2023-09-27)
    Cyber-physical IoT nodes located in environments which are resource-constrained and physically hard to access, like the Arctic tundra, must achieve long operational lifetimes from a single battery and report data over data networks. The nodes sleep most of the time, and only wake up to perform mission tasks, including reporting data. However, networks can become unavailable, or have low bandwidth ...
  • Image Inpainting With Hypergraphs for Resolution Improvement in Scanning Acoustic Microscopy 

    Somani, Ayush; Banerjee, Pragyan; Rastogi, Manu; Habib, Anowarul; Agarwal, Krishna; Prasad, Dilip Kumar (Journal article; Tidsskriftartikkel; Peer reviewed, 2023-08-14)
    Scanning Acoustic Microscopy (SAM) uses high-frequency acoustic waves to generate non-ionizing, label-free images of the surface and internal structures of industrial objects and biological specimens. The resolution of SAM images is limited by several factors such as the frequency of excitation signals, the signal-to-noise ratio, and the pixel size. We propose to use a hypergraphs image inpainting ...
  • Predicting Meibomian Gland Dropout and Feature Importance Analysis with Explainable Artificial Intelligence 

    Fineide, Fredrik; Storås, Andrea; Riegler, Michael Alexander; Utheim, Tor Paaske (Journal article; Tidsskriftartikkel; Peer reviewed, 2023-07-17)
    Dry eye disease is a common and potentially debilitating medical condition. Meibum secreted from the meibomian glands is the largest contributor to the outermost, protective lipid layer of the tear film. Dysfunction of the meibomian glands is the most common cause of dry eye disease. As meibomian gland dysfunction progresses, gradual atrophy of the glands is observed. The meibomian glands are ...
  • Algorithm for predicting valvular heart disease from heart sounds in an unselected cohort 

    Waaler, Per Niklas Benzler; Melbye, Hasse; Schirmer, Henrik; Johnsen, Markus Kreutzer; Dønnem, Tom; Ravn, Johan Fredrik; Andersen, Stian; Davidsen, Anne Herefoss; Aviles Solis, Juan Carlos; Stylidis, Michael; Bongo, Lars Ailo Aslaksen (Journal article; Tidsskriftartikkel; Peer reviewed, 2024-01-24)
    Objective: This study aims to assess the ability of state-of-the-art machine learning algorithms to detect valvular heart disease (VHD) from digital heart sound recordings in a general population that includes asymptomatic cases and intermediate stages of disease progression.<p> <p>Methods: We trained a recurrent neural network to predict murmurs from heart sound audio using annotated recordings ...
  • De-identifying Norwegian Clinical Text using Resources from Swedish and Danish 

    Lamproudis, Anastasios; Mora, Sara; Olsen Svenning, Therese; Torsvik, Torbjørn; Chomutare, Taridzo Fred; Ngo, Phuong Dinh; Dalianis, Hercules (Journal article; Tidsskriftartikkel, 2023)
    The lack of relevant annotated datasets represents one key limitation in the application of Natural Language Processing techniques in a broad number of tasks, among them Protected Health Information (PHI) identification in Norwegian clinical text. In this work, the possibility of exploiting resources from Swedish, a very closely related language, to Norwegian is explored. The Swedish dataset is ...
  • Eventually-Consistent Replicated Relations and Updatable Views 

    Thomassen, Joachim; Yu, Weihai (Chapter; Bokkapittel, 2023-08-31)
    Distributed systems have to live with weak consistency, such as eventual consistency, if high availability is the primary goal and network partitioning is unexceptional. Local-first applications are examples of such systems. There is currently work on local-first databases where the data are asynchronously replicated on multiple devices and the replicas can be locally updated even when the devices ...
  • Validation of ESDS Using Epidemic-Based Data Dissemination Algorithms 

    Guegan, Loic; Rais, Issam; Anshus, Otto Johan (Journal article; Tidsskriftartikkel, 2023-09-27)
    The study of Distributed Systems (DS) is important as novel solutions in this area impact many sub-fields of Computer Science. Although, studying DS is not an easy task. A common approach is to deploy a test-bed to perform a precise evaluation of the system. This can be costly and time consuming for large scale platforms. Another solution is to perform network simulations, allowing for more flexibility ...
  • Towards Data Dissemination Policy Prediction for Constrained Environments Using Analytics 

    Guegan, Loic; Rais, Issam; Anshus, Otto Johan (Journal article; Tidsskriftartikkel, 2023-09-27)
    <p>In Cyber-Physical Systems (CPS) such as Wireless Sensors Networks (WSN), disseminating data is crucial. Under energy constraints with limited communications capabilities, performing data dissemination is challenging. In such contexts, common data dissemination methods cannot be used. Nodes must rely on device-to-device communications policies to mitigate the impact of communications on the nodes ...
  • Image Inpainting With Hypergraphs for Resolution Improvement in Scanning Acoustic Microscopy 

    Somani, Ayush; Banerjee, Pragyan; Rastogi, Manu; Habib, Anowarul; Agarwal, Krishna; Prasad, Dilip Kumar (Journal article; Tidsskriftartikkel; Peer reviewed, 2023-08-14)
    Scanning Acoustic Microscopy (SAM) uses high-frequency acoustic waves to generate non-ionizing, label-free images of the surface and internal structures of industrial objects and biological specimens. The resolution of SAM images is limited by several factors such as the frequency of excitation signals, the signal-to-noise ratio, and the pixel size. We propose to use a hypergraphs image inpainting ...
  • Sport and Nutrition Digital Analysis: A Legal Assessment 

    Juliussen, Bjørn Aslak; Rui, Jon Petter; Johansen, Dag (Journal article; Tidsskriftartikkel; Peer reviewed, 2023-03-29)
    This paper presents and evaluates legal aspects related to digital technologies applied in the elite soccer domain. Data Protection regulations in Europe clearly indicate that compliance-by-design is needed when developing and deploying such technologies. This is particularly true when health data is involved, but a complicating factor is that the distinction between what is health data or not is ...
  • Accurate Lightweight Calibration Methods for Mobile Low-Cost Particulate Matter Sensors 

    Jørstad, Per Martin; Wojcikowski, Marek; Cao, Tuan-Vu; Lepioufle, Jean-Marie; Wojtkiewicz, Krystian; Ha, Hoai Phuong (Chapter; Bokkapittel, 2023-09-05)
    <p>Monitoring air pollution is a critical step towards improving public health, particularly when it comes to identifying the primary air pollutants that can have an impact on human health. Among these pollutants, particulate matter (PM) with a diameter of up to 2.5 μm (or PM2.5) is of particular concern, making it important to continuously and accurately monitor pollution related to PM. The emergence ...
  • Sleep Monitoring with Wearable Sensor Data in an eCoach Recommendation System: A Conceptual Study with Machine Learning Approach 

    Chatterjee, Ayan; Prinz, Andreas; Pahari, Nibedita; Das, Jishnu; Riegler, Michael (Chapter; Bokkapittel, 2023-04-25)
    The collective effects of sleep loss and sleep disorders are correlated with many adverse health consequences, including increased risk of high blood pressure, obesity, diabetes, depressive state, and cardiovascular symptoms. Research in eHealth can provide methods to enrich personal health care with information and communication technologies (ICTs). An eCoach system may allow people to manage a ...
  • Multi-Agent Collision Avoidance Method Using Fuzzy Risk Estimation and Information Sharing in Unknown Environments 

    Håkansson, Anne Eva Margareta; Karlsen, Randi; Bremdal, Bernt Arild; Dundar, Yigit Can (Chapter; Bokkapittel, 2023-09-22)
    Automated vehicles within Industry 4.0 are used as logistics units where they can move resources from one place to another safely and efficiently. The automated vehicles can be tasked to work in unknown environments where collision-free navigation is challenging due to uncertainty and lack of environmental information. Collisions can damage equipment and may even cause harm to human workers sharing ...

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