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  • 1.
    Sjögren, Thomas
    et al.
    Blekinge Institute of Technology, School of Engineering, Department of Electrical Engineering.
    Vu, Viet
    Blekinge Institute of Technology, School of Engineering, Department of Electrical Engineering.
    Pettersson, Mats
    Blekinge Institute of Technology, School of Engineering, Department of Electrical Engineering.
    Wang, Feng
    Blekinge Institute of Technology, School of Engineering, Department of Electrical Engineering.
    Murdin, Daniel
    Gustavsson, Anders
    Ulander, Lars
    Forest Clutter Suppression for Moving Target Detection in UHF Dual Channel SAR2013Conference paper (Refereed)
  • 2.
    Sjögren, Thomas
    et al.
    Blekinge Institute of Technology, School of Engineering, Department of Electrical Engineering.
    Vu, Viet
    Blekinge Institute of Technology, School of Engineering, Department of Electrical Engineering.
    Pettersson, Mats
    Blekinge Institute of Technology, School of Engineering, Department of Electrical Engineering.
    Wang, Feng
    Blekinge Institute of Technology, School of Engineering, Department of Electrical Engineering.
    Murdin, Daniel
    Gustavsson, Anders
    Ulander, Lars
    Suppression of Clutter in Multichannel SAR GMTI2014In: IEEE Transactions on Geoscience and Remote Sensing, ISSN 0196-2892, E-ISSN 1558-0644, Vol. 52, no 7, p. 4005-4013Article in journal (Refereed)
    Abstract [en]

    In this paper, results of moving target detection in multichannel UHF-band Synthetic Aperture Radar (SAR) data are shown. The clutter suppression is done using Finite Impulse Response (FIR) filtering of multichannel SAR in combination with a 2-stage Fast Backprojection (FBP) algorithm to focus the moving target using relative speed. The FIR filter coefficients are chosen with the use of STAP filtering. Two parameters are used for target focusing, target speed in range and in azimuth. When the target is focused, both speed parameters of the target are found. In the experimental results, two channels were used in order to suppress clutter. In the resulting SAR images it is obvious that very strong scatterers and the forest areas have been suppressed in comparison to the moving target in the image scene. The gain obtained can be measured using SCNR gain, which is about 19dB. Another way to measure signal processing gain is the ability to suppress the strongest reflecting object in the SAR scene. The gain of target in relation to this object is 25dB. This shows that using UHF-band SAR GMTI for suppressing forest and increasing the target signal can work.

  • 3.
    Wang, Shujun
    Blekinge Institute of Technology, Faculty of Engineering, Department of Industrial Economics.
    Determinants of mobile apps downloads: A systematic literature review2017In: Proceedings of the 11th European Conference on Information Systems Management, ECISM 2017, Academic Conferences and Publishing International Limited , 2017, p. 353-360Conference paper (Refereed)
    Abstract [en]

    Mobile apps have grown fast since 2008, and mobile app global revenues amounted to 69.7 billion U.S. dollars in 2015. Hence, knowledge of mobile apps is of importance to mobile app developers, mobile app platform producers, and consumers following its size and expected growth. Mobile app downloads, i.e. how many times an app has been downloaded, are often claimed to reflect an app's popularity. A systematic literature review is conducted to identify what are the determinants of mobile app downloads, and to give guidance for future research. A total of 27 articles are selected as the final sample based on searches in four scientific databases. The selected articles identify a diverse set of determinants. In order to present results of selected articles in a more systematic way, I analyse the results of selected articles by three themes, which are consumers' intention, app developer strategy, and platform information (e.g. ratings, reviews and ranking), according to their purposes or research questions. The determinants of mobile app downloads summarized from the selected literatures are: perceived usefulness, perceived enjoyment, peer information, perceived ease of use, user interests, perceived risk, freemium strategy, advertisement, price, app selling category, app type, consumer ratings (valence, volume and variance), character of consumer reviews, top list ranking, etc. The dominant method among these articles is regression analysis. The review also shows that there is a need for better theoretical explanation to why consumers download mobile apps. Different robustness checks by econometric models can be done to test different determinants of mobile app downloads in further studies. Determinants from mobile app developers and platform information are less studied in previous researches, thus mobile app business strategy, consumer ratings and reviews, and mobile app categories need to be explored deeper in future researches. The relative importance of different determinants under different circumstances need to be investigated in more depth in future research.

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