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Name
Document Server@UHasselt
Type
Institutional Repository
Items
10390 Publications
Compatibility
OpenAIRE 2.0+ (DRIVER OA, EC funding)
OAI-PMH
https://doclib.uhasselt.be/oai/driver
More information
Detailed data provider information (OpenDOAR)

 

  • Inter-firm R&D networks in the global software industry: An overview of major trends and patterns

    This paper presents an analysis of some major historical trends in inter-firm R&D partnering in the international software industry during the period 1970-1999. Our research demonstrates an overall growth pattern of newly made R&D partnerships and reveals the important role played by leading firms. We also examine the emergence of various R&D networks in the software industry, indicating the change from sparse and disconnected inter-firm R&D networks to dense and well-connected networks since...

    Retail lighting: shedding light on people, products/brands and spaces

    To understand where we are at, and to learn from the past, a brief history of retail lighting, with the focus on atmosphere will be given. Next, via the psychological aspects of lighting, its importance and the human-environment interaction will be explained. A study will be presented which shows the impact of lighting on the perception of atmosphere, the emotions and behaviour.

    Real-time, Adaptive Plane Sweeping for Free Viewpoint Navigation in Soccer Scenes

    In this dissertation, we present a system to generate a novel viewpoint using a virtual camera, specifically for soccer scenes. We demonstrate the applicability for following players, freezing the scene, generating 3D images, et cetera. The method is demonstrated and investigated for 2 camera arrangements, i.e. a curved and a linear setup, where the distance between the cameras can be up to 10 meters. The virtual camera should be located on a position between the real camera positions. ...

    Modeling multivariate, overdispersed binomial data with additive and multiplicative random effects

    When modelling multivariate binomial data, it often occurs that it is necessary to take into consideration both clustering and overdispersion, the former arising from the dependence between data, and the latter due to the additional variability in the data not prescribed by the distribution. If interest lies in accommodating both phenomena at the same time, we can use separate sets of random effects that capture the within-cluster association and the extra variability. In particular, the ...
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