Filtreler

2007 - 20092
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Bölüm: Işık Üniversitesi, Mühendislik Fakültesi, Elektrik-Elektronik Mühendisliği Bölümü × Konu: Data mining ×

Arama Sonuçları

Listeleniyor 1 - 2 / 2
  • Küçük Resim
    Yayın
    On the extraction of the channel allocation information in spectrum pooling systems
    (IEEE, 2007-04) Öner, Mustafa Mengüç; Jondral, Friedrich K.
    The spectrum pooling strategy allows a license owner to share a part of his licensed spectrum with a secondary wireless system (the rental system, RS) during its idle times. The coexistence of two mobile systems on the same frequency band poses many new challenges, one of which is the reliable extraction of the channel allocation information (CAI), i.e. the channel occupation of the licensed system (LS). This paper presents a strategy for the extraction of the CAI based on exploiting the distinct cyclostationary characteristics of the LS and RS signals and demonstrates, via simulations, its application on a specific spectrum pooling scenario, where the LS is a GSM network and the RS is an OFDM based WLAN system.
  • Küçük Resim
    Yayın
    Generative and discriminative methods using morphological information for sentence segmentation of Turkish
    (IEEE-INST Electrical Electronics Engineers Inc, 2009-07) Güz, Ümit; Favre, Benoit; Hakkani Tür, Dilek; Tür, Gökhan
    This paper presents novel methods for generative, discriminative, and hybrid sequence classification for segmentation of Turkish word sequences into sentences. In the literature, this task is generally solved using statistical models that take advantage of lexical information among others. However, Turkish has a productive morphology that generates a very large vocabulary, making the task much harder. In this paper, we introduce a new set of morphological features, extracted from words and their morphological analyses. We also extend the established method of hidden event language modeling (HELM) to factored hidden event language modeling (fHELM) to handle morphological information. In order to capture non-lexical information, we extract a set of prosodic features, which are mainly motivated from our previous work for other languages. We then employ discriminative classification techniques, boosting and conditional random fields (CRFs), combined with fHELM, for the task of Turkish sentence segmentation.