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  • 1.
    Ahlgren, Bengt
    et al.
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Jonasson, Arndt
    RISE, Swedish ICT, SICS, Software and Systems Engineering Laboratory.
    Scalable Live TV Distribution with NetInf to Android Devices (poster/demo)2014Conference paper (Refereed)
    Download full text (pdf)
    fulltext
  • 2.
    Ahlgren, Bengt
    et al.
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Jonasson, Arndt
    RISE, Swedish ICT, SICS, Software and Systems Engineering Laboratory.
    Ohlman, Börje
    Ericsson AB, Sweden.
    HTTP Live Streaming over NetInf Transport2014Conference paper (Refereed)
    Abstract [en]

    We modified a commercial Android TV app to use NetInf ICN transport. It was straightforward to adapt the standard HTTP Live Streaming to NetInf naming and network service. We demonstrate that NetInf's in-network caching and request aggregation result in efficient live TV distribution.

  • 3.
    Ekman, Jan
    et al.
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Holst, Anders
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Jonasson, Arndt
    RISE, Swedish ICT, SICS, Software and Systems Engineering Laboratory.
    Condition based maintenance of trains doors2011Report (Other academic)
    Abstract [en]

    As part of the project DUST financed by Vinnova, we have investigated whether event data generated on trains can be used for finding evidence of wear on train doors. We have compared the event data and maintenance reports relating to doors of Regina trains. Although some interesting relations were found, the overall result is that the information in event data about wear of doors is very limited.

    Download full text (pdf)
    FULLTEXT01
  • 4.
    Stöggl, Thomas Leonhard
    et al.
    University of Salzburg, Austria; Mid Sweden University, Sweden.
    Holst, Anders
    RISE, Swedish ICT, SICS. KTH Royal Institute of Technology, Sweden.
    Jonasson, Arndt
    RISE, Swedish ICT, SICS.
    Andersson, Erik Petrus
    Mid Sweden University, Sweden.
    Wunsch, Tobias
    University of Salzburg, Austria.
    Norström, Christer
    RISE, Swedish ICT, SICS.
    Holmberg, Hans Christer
    Mid Sweden University, Sweden; Swedish Olympic Committee, Sweden.
    Automatic classification of the Sub-Techniques (Gears) used in cross-country ski skating employing a mobile phone2014In: Sensors, E-ISSN 1424-8220, Vol. 14, no 11, p. 20589-20601Article in journal (Refereed)
    Abstract [en]

    The purpose of the current study was to develop and validate an automatic algorithm for classification of cross-country (XC) ski-skating gears (G) using Smartphone accelerometer data. Eleven XC skiers (seven men, four women) with regional-to-international levels of performance carried out roller skiing trials on a treadmill using fixed gears (G2left, G2right, G3, G4left, G4right) and a 950-m trial using different speeds and inclines, applying gears and sides as they normally would. Gear classification by the Smartphone (on the chest) and based on video recordings were compared. Formachine-learning, a collective database was compared to individual data. The Smartphone application identified the trials with fixed gears correctly in all cases. In the 950-m trial, participants executed 140 ± 22 cycles as assessed by video analysis, with the automatic Smartphone application giving a similar value. Based on collective data, gears were identified correctly 86.0% ± 8.9% of the time, a value that rose to 90.3% ± 4.1% (P < 0.01) with machine learning from individual data. Classification was most often incorrect during transition between gears, especially to or from G3. Identification was most often correct for skiers who made relatively few transitions between gears. The accuracy of the automatic procedure for identifying G2left, G2right, G3, G4left and G4right was 96%, 90%, 81%, 88% and 94%, respectively. The algorithm identified gears correctly 100% of the time when a single gear was used and 90% of the time when different gears were employed during a variable protocol. This algorithm could be improved with respect to identification of transitions between gears or the side employed within a given gear.

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