Viser treff 325-344 av 625

    • M-CDS: Mobile Carbohydrate Delivery System 

      Puvanendran, Neethan (Mastergradsoppgave; Master thesis, 2023-06-20)
      When patients with type 1 diabetes (T1D) are physically active, they encounter an issue with keeping their blood glucose (BG) stable. Generally, their blood glucose level (BGL) will drop, causing hypoglycaemia which can have fatal consequences. The simple solution is to consume carbohydrates in the form of liquids or food, but during physical activities, it can be difficult to follow their BGL at ...
    • M2S and CAIR. Image based information retrieval in mobile environments. 

      Aarbakke, Anne Staurland (Master thesis; Mastergradsoppgave, 2007-05-01)
      Images are commonly used on a daily basis for research, information and entertainment. The introduction of digital cameras and especially the incorporation of cameras in mobile phones makes people able to snap photos almost everywhere at any time since their mobile phone is almost always brought with them. The fast evolution in hardware enables users to store large image collection without high ...
    • 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 ...
    • Machine Learning for Hydropower Scheduling: State of the Art and Future Research Directions 

      Bordin, Chiara; Skjelbred, Hans Ivar; Kong, Jiehong; Yang, Zhirong (Journal article; Tidsskriftartikkel; Peer reviewed, 2020-10-02)
      This paper investigates and discusses the current and future role of machine learning (ML) within the hydropower sector. An overview of the main applications of ML in the field of hydropower operations is presented to show the most common topics that have been addressed in the scientific literature in the last years. The objective is to provide recommendations for novel research directions that can ...
    • Making your devices speak. Integration between Amazon Alexa and the Managed IoT Cloud 

      Holden, Thomas (Master thesis; Mastergradsoppgave, 2018-06-01)
      Speech recognition and communication between humans and machines are increasingly popular today. Several companies already have products in this market segment. The Managed IoT Cloud (MIC) platform is a complete ecosystem for management of Internet of Things (IOT) devices, data storage and analysis of data. However, the platform lacks an integration with a personal assistant to introduces ...
    • Management of large geospatial datasets 

      Lau, Ka Hin (Mastergradsoppgave; Master thesis, 2022-05-15)
      In large simulations, like predicting the movement of ocean particles, it is common that simulation executions are related when they share one or more inputs. When the number of simulations increases, it becomes harder for users who run the simulations to keep track of all the simulations. Also, more storage spaces are wasted if there are multiple copies of the same input files. This thesis ...
    • Mario. A system for iterative and interactive processing of biological data 

      Ernstsen, Martin (Master thesis; Mastergradsoppgave, 2013-11-15)
      This thesis address challenges in metagenomic data processing on clusters of computers; in particular the need for interactive response times during development, debugging and tuning of data processing pipelines. Typical metagenomics pipelines batch process data, and have execution times ranging from hours to months, making configuration and tuning time consuming and impractical. We have analyzed ...
    • The Mask: Masking the effects of Edge Nodes being unavailable 

      Zhakun, Ilia (Mastergradsoppgave; Master thesis, 2021-11-15)
      The arctic tundra is observed to collect data to be used for climate research. Data can be collected by cyber-physical computers with sensors. However, the arctic tundra has a limited availability of energy. Consequently, the nodes rely on batteries and sleep most of the time to increase the battery-limited operational lifetime. In addition, only a few nodes can expect to be in reach of a back-haul ...
    • Masking the Effects of Delays in Human-to-Human Remote Interaction 

      Su, Fei; Bjørndalen, John Markus; Ha, Hoai Phuong; Anshus, Otto (Chapter; Bokkapittel, 2014)
      Humans can interact remotely with each other through computers. Systems supporting this include teleconferencing, games and virtual environments. There are delays from when a human does an action until it is reflected remotely. When delays are too large, they will result in inconsistencies in what the state of the interaction is as seen by each participant. The delays can be reduced, but they cannot ...
    • Mearka - Architecting and evaluation of a Sports Video Tagging Software Toolkit 

      Torkelsen, Alexander (Mastergradsoppgave; Master thesis, 2023-06-01)
      In the past decade, substantial advancements have been achieved in effectively utilizing video surveillance and associated analysis technologies within the realm of sports. This progress has been particularly noteworthy in elite sports, where the exploitation of athletes’ digital footprints for sports analytics has emerged as a catalytic factor, ushering in a paradigm shift in comprehending and ...
    • Measuring Physical Activity with Sensors : A Qualitative Study 

      Fisterer, Bernhard; Dias, Andrê Fernando; Hartvigsen, Gunnar; Lamla, Gregor; Kuhn, Klaus A.; Horsch, Alexander (Journal article; Tidsskriftartikkel; Peer reviewed, 2009)
    • MELT: The multidimensional key-value store performance evaluation framework. Melt: memory, energy, latency and throughput 

      Blomli-Edvardsen, Tobias (Master thesis; Mastergradsoppgave, 2017-06-02)
      1 Abstract Key-value stores have a very large variation in their design and implementation, while still adhering to the key-value abstraction. The available generic benchmarks cannot truly represent the performance a key-value store will have with a specific application, unless your application happens to have the exact same configuration and workloads as the benchmark. Moreover, most benchmarks ...
    • Meta-learning with implicit gradients in a few-shot setting for medical image segmentation 

      Khadka, Rabindra; Jha, Debesh; Riegler, Michael A.; Hicks, Steven; Thambawita, Vajira; Ali, Sharib; Halvorsen, Pål (Journal article; Tidsskriftartikkel; Peer reviewed, 2022-01-12)
      Widely used traditional supervised deep learning methods require a large number of training samples but often fail to generalize on unseen datasets. Therefore, a more general application of any trained model is quite limited for medical imaging for clinical practice. Using separately trained models for each unique lesion category or a unique patient population will require sufficiently large curated ...
    • META-pipe cloud setup and execution 

      Agafonov, Aleksander; Mattila, Kimmo; Tuan, Cuong Duong; Tiede, Lars; Raknes, Inge Alexander; Bongo, Lars Ailo Aslaksen (Journal article; Tidsskriftartikkel; Peer reviewed, 2018-01-18)
      META-pipe is a complete service for the analysis of marine metagenomic data. It provides assembly of high-throughput sequence data, functional annotation of predicted genes, and taxonomic profiling. The functional annotation is computationally demanding and is therefore currently run on a high-performance computing cluster in Norway. However, additional compute resources are necessary to open the ...
    • Metadata state and history service for datasets. Enable extracting, storing and access to metadata about a dataset over time. 

      Hansen, Roberth (Master thesis; Mastergradsoppgave, 2018-05-14)
      Distributed Arctic Observatory (DAO) aims to automate, streamline and improve the collection, storage and analysis of images, video and weather measurements taken on the arctic tundra. Automating the process means that there are no human users that needs to be involved in the process. This leads to a loss of monitoring capabilities of the process. There are insufficient tools that allow the human ...
    • The metagenomic data life-cycle: standards and best practices 

      Ten Hoopen, Petra; Finn, Robert D.; Bongo, Lars Ailo; Corre, Erwan; Fosso, Bruno; Meyer, Folker; Mitchell, Alex; Pelletier, Eric; Pesole, Graziano; Santamaria, Monica; Willassen, Nils Peder; Cochrane, Guy (Journal article; Tidsskriftartikkel; Peer reviewed, 2017-08-01)
      Metagenomics data analyses from independent studies can only be compared if the analysis workflows are described in a harmonized way. In this overview, we have mapped the landscape of data standards available for the description of essential steps in metagenomics: (i) material sampling, (ii) material sequencing, (iii) data analysis, and (iv) data archiving and publishing. Taking examples from marine ...
    • Metastatic Breast Cancer and Pre-Diagnostic Blood Gene Expression Profiles—The Norwegian Women and Cancer (NOWAC) Post-Genome Cohort 

      Holsbø, Einar; Olsen, Karina Standahl (Journal article; Tidsskriftartikkel; Peer reviewed, 2020-10-15)
      Breast cancer patients with metastatic disease have a higher incidence of deaths from breast cancer than patients with early-stage cancers. Recent findings suggest that there are differences in immune cell function between metastatic and non-metastatic cases, even years before diagnosis. We have analyzed whole blood gene expression by Illumina bead chips in blood samples taken using the PAXgene blood ...
    • Method for Designing Semantic Annotation of Sepsis Signs in Clinical Text 

      Yan, Melissa Y.; Gustad, Lise Tuset; Høvik, Lise Husby; Nytrø, Øystein (Chapter; Bokkapittel, 2023)
      Annotated clinical text corpora are essential for machine learning studies that model and predict care processes and disease progression. However, few studies describe the necessary experimental design of the annotation guideline and annotation phases. This makes replication, reuse, and adoption challenging. Using clinical questions about sepsis, we designed a semantic annotation guideline to ...
    • Methods and Evaluation Criteria for Apps and Digital Interventions for Diabetes Self-Management: Systematic Review 

      Larbi, Dillys; Randine, Pietro; Årsand, Eirik; Antypas, Konstantinos; Bradway, Meghan; Gabarron, Elia (Journal article; Tidsskriftartikkel; Peer reviewed, 2020-07-06)
      <i>Background</i>: There is growing evidence that apps and digital interventions have a positive impact on diabetes self-management. Standard self-management for patients with diabetes could therefore be supplemented by apps and digital interventions to increase patients’ skills. Several initiatives, models, and frameworks suggest how health apps and digital interventions could be evaluated, but ...
    • Metrix: Real-time Analysis of Physical Performance Parameters in Elite Soccer 

      Andreassen, Kim Hartvedt (Master thesis; Mastergradsoppgave, 2018-06-01)
      In recent years, technology has had a vast impact on the sports industry, particularly in soccer. Elite soccer teams utilize digital information systems to quantify performance metrics, in order to assess their strengths and weaknesses. Applied methods mostly rely on post-game analytics, allowing coaches to review games in retrospect and implement corrections to their team thereafter. However, this ...