Viser treff 152-171 av 625

    • Eatnu: a storage system for evaluating and persisting sensor data 

      Stenhaug, Magnus (Master thesis; Mastergradsoppgave, 2014-06-01)
      The amount of information generated exceeeds the current available storage. Big Data, the Internet of Things and the increasing popularity of self-tracking gadgets call for new storage solutions to manage and analyze the data. To handle the constant flow of information, we have implemented Eatnu. Eatnu is a storage system designed to handle large data streams, where programmers can specify what ...
    • EC3 - Edge Command-Control-Communication System for Arctic Observatories 

      Michalik, Lukasz Sergiusz (Master thesis; Mastergradsoppgave, 2017-05-13)
      This paper presents a prototype of a system for automated observations of flora and fauna in the Arctic. Currently applied methods of observation depend mostly on systems (usually consisting of a camera unit, a motion detection sensor and a memory card) that are left unattended in remote locations during extended periods of data gathering. The main problem with such approach is that no remote control ...
    • EDMON - A backend server for an infection detection system monitoring individuals with type 1 diabetes 

      Coucheron, Sverre (Master thesis; Mastergradsoppgave, 2019-05-31)
      There are a growing number of adults with diabetes worldwide. Within 2045 it is expected to become over 600 million individuals. Since there are no known cures for diabetes, self-monitoring and self-recording are often used to manage the condition. Having tools such as mobile applications allow individuals to do this. The world and society face a significant health threat from communicable diseases, ...
    • EDMON - a system architecture for real-time infection monitoring and outbreak detection based on self-recorded data from people with type 1 diabetes: system design and prototype implementation 

      Coucheron, Sverre; Woldaregay, Ashenafi Zebene; Årsand, Eirik; Botsis, Taxiarchis; Hartvigsen, Gunnar (Journal article; Tidsskriftartikkel; Peer reviewed, 2019-11)
      Infection incidences in people with diabetes can create sever health complications mainly due to the effect of stress hormones, such as cortisol and adrenaline, which increases glucose production and insulin resistance in the body. The proposed electronic disease surveillance monitoring network (EDMON) relies on self-recorded data from people with Type 1 diabetes and dedicated algorithms to detect ...
    • Educating the energy informatics specialist: opportunities and challenges in light of research and industrial trends 

      Bordin, Chiara; Mishra, Sambeet; Safari, Amir; Eliassen, Frank (Journal article; Tidsskriftartikkel; Peer reviewed, 2021-05-30)
      Contemporary energy research is becoming more interdisciplinary through the involvement of technical, economic, and social aspects that must be addressed simultaneously. Within such interdisciplinary energy research, the novel domain of energy informatics plays an important role, as it involves different disciplines addressing the socio-techno-economic challenges of sustainable energy and power ...
    • Effectiveness of LRB in Curved Bridge Isolation: A Numerical Study 

      Gupta, Praveen Kumar; Ghosh, Goutam; Kumar, Virendra; Paramasivam, Prabhu; Dhanasekaran, Seshathiri (Journal article; Tidsskriftartikkel; Peer reviewed, 2022-11-07)
      Lead Rubber Bearings (LRBs) represent one of the most widely employed devices for the seismic protection of structures. However, the effectiveness of the same in the case of curved bridges has not been judged well because of the complexity involved in curved bridges, especially in controlling torsional moments. This study investigates the performance of an LRB-isolated horizontally curved ...
    • An efficient bill-of-materials algorithm 

      Khalaila, Ahmad; Eliassen, Frank (Research report; Forskningsrapport, 1997-04)
      A large class of linear recursive queries compute the bill-of-materials of database relations.This paper presents a novel algorithm that computes the bill-of-materials of its argument's (database) relation. The algorithm uses a special join operation that accumulates the cost of composite parts, without constructing the transitive closure of the argument relation, thus saving time and space. We ...
    • Efficient bill-of-materials algorithms 

      Beeri, Catriel; Khalaila, Ahmad; Eliassen, Frank (Research report; Forskningsrapport, 1996-09-01)
      It has been shown that every linearly recursive database query can be expressed as a transitive closure possibly preceded and followed by relational algebraic operations. A large class of such queries computes the bill-of-materials of database relations. This paper presents efficient sequential and distributed algorithms that compute the bill-of-materials of a database relation. These algorithms ...
    • Efficient concurrent search trees using portable fine-grained locality 

      Ha, Hoai Phuong; Anshus, Otto; Umar, Ibrahim (Journal article; Tidsskriftartikkel; Peer reviewed, 2019-01-14)
      Concurrent search trees are crucial data abstractions widely used in many important systems such as databases, file systems and data storage. Like other fundamental abstractions for energy-efficient computing, concurrent search trees should support both high concurrency and fine-grained data locality in a platform-independent manner. However, existing portable fine-grained locality-aware search trees ...
    • Efficient disease detection in gastrointestinal videos – global features versus neural networks 

      Pogorelov, Konstantin; Riegler, Michael; Eskeland, Sigrun Losada; de Lange, Thomas; Johansen, Dag; Griwodz, Carsten; Schmidt, Peter Thelin; Halvorsen, Pål (Journal article; Tidsskriftartikkel; Peer reviewed, 2017-07-19)
      Analysis of medical videos from the human gastrointestinal (GI) tract for detection and localization of abnormalities like lesions and diseases requires both high precision and recall. Additionally, it is important to support efficient, real-time processing for live feedback during (i) standard colonoscopies and (ii) scalability for massive population-based screening, which we conjecture can be done ...
    • Efficient intra-node Communication for Chip Multiprocessors 

      Henriksen, Torje Starbo (Master thesis; Mastergradsoppgave, 2008-10-15)
      The microprocessor industry has reached limitations of sequential processing power due to power-efficiency and heat problems. With the integrated-circuit technology moving forward, chip-multithreading has become the trend, increasing parallel processing power. The shift of focus has resulted in the vast majority of supercomputers having chip-multiprocessors. While the high performance computing ...
    • Efficient live and on-demand tiled HEVC 360 VR video streaming 

      Jeppsson, Mattis; Espeland, Håvard; Kupka, Tomas; Langseth, Ragnar; Petlund, Andreas; Peng, Qiaoqiao; Xue, Chuansong; Johansen, Dag; Pogorelov, Konstantin; Stensland, Håkon Kvale; Griwodz, Carsten; Riegler, Michael; Halvorsen, Pål (Journal article; Tidsskriftartikkel; Peer reviewed, 2019)
      360∘ panorama video displayed through Virtual Reality (VR) glasses or large screens offers immersive user experiences, but as such technology becomes commonplace, the need for efficient streaming methods of such high-bitrate videos arises. In this respect, the attention that 360∘ panorama video has received lately is huge. Many methods have already been proposed, and in this paper, we shed more light ...
    • Efficient quantile tracking using an oracle 

      Hammer, Hugo Lewi; Yazidi, Anis; Riegler, Michael; Rue, Håvard (Journal article; Tidsskriftartikkel; Peer reviewed, 2022-04-14)
      Concept drift is a well-known issue that arises when working with data streams. In this paper, we present a procedure that allows a quantile tracking procedure to cope with concept drift. We suggest using expected quantile loss, a popular loss function in quantile regression, to monitor the quantile tracking error, which, in turn, is used to efficiently adapt to concept drift. The suggested ...
    • Embarrassingly Distributed Computing for Symbiotic Weather Forecasts 

      Fjukstad, Bård; Bjørndalen, John Markus; Anshus, Otto (Journal article; Tidsskriftartikkel; Peer reviewed, 2013)
    • Embedded analytics of animal images 

      Thomassen, Sigurd (Master thesis; Mastergradsoppgave, 2017-12-14)
      Due to the large increase of image data in animal surveillance, an effective and efficient way of labeling said data is required. Over the past few years the Climate-ecological Observatory for Arctic Tundra (COAT) project have deployed dozens of cameras in eastern Finnmark, Norway during winter, which have resulted in a large volume of wildlife images which is used to document the effects of climate ...
    • Emotionally charged text classification with deep learning and sentiment semantic 

      Huan, Jeow Li; Sekh, Arif Ahmed; Quek, Chai; Prasad, Dilip K. (Journal article; Tidsskriftartikkel; Peer reviewed, 2021-09-28)
      Text classification is one of the widely used phenomena in different natural language processing tasks. State-of-the-art text classifiers use the vector space model for extracting features. Recent progress in deep models, recurrent neural networks those preserve the positional relationship among words achieve a higher accuracy. To push text classification accuracy even higher, multi-dimensional ...
    • Employee-driven digital innovation: A systematic review and a research agenda 

      Opland, Leif Erik; Pappas, Ilias; Engesmo, Jostein; Jaccheri, Maria Letizia (Journal article; Tidsskriftartikkel; Peer reviewed, 2022-02-01)
      As the digital shift in society affects both private and public organizations, the role of digital innovation is critical if digital transformations are to succeed. Research has developed models to explain how digital innovation affects organizations and societies. During the last ten years, employee-driven innovation has emerged as a new approach to explain innovation. Through this systematic ...
    • End-to-end Trainable Ship Detection in SAR Images with Single Level Features 

      Tiller, Markus (Master thesis; Mastergradsoppgave, 2022-06-01)
      Kongsberg Satellite Services (KSAT) use machine learning and manual analysis done by synthetic aperture radar (SAR) specialists on SAR images in real time to provide a ship detection service. KSATs current machine learning model has a limited ability to distinguish ships close to each other. For this reason, we aim to employ an end-to-end trainable object detection model, as they can better ...
    • Enforcing Privacy Policies with Meta-Code 

      Johansen, Håvard; Birrell, Eleanor; Van Renesse, Robbert; Schneider, Fred B.; Stenhaug, Magnus; Johansen, Dag (Konferansebidrag; Conference object, 2015)
      This paper proposes a mechanism for expressing and enforcing security policies for shared data. Security policies are expressed as stateful meta-code operations; meta-code can express a broad class of policies, including access-based policies, use-based policies, obligations, and sticky policies with declassification. The meta-code is interposed in the filesystem access path to ensure policy ...
    • Engaging Social Media Users with Health Education and Physical Activity Promotion 

      Gabarron, Elia; Larbi, Dillys; Årsand, Eirik; Wynn, Rolf (Journal article; Tidsskriftartikkel; Peer reviewed, 2021)
      Health-dedicated groups on social media provide different contents and social support to their peers. Our objective is to analyze users’ engagement with health education and physical activity promotion posts according to the expressed social support and social media. All health education and physical activity promotion posts on Facebook, Twitter, and Instagram during 2017–2019 by a diabetes association ...