Bilgisayar Mühendisliği Bölümü Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.11779/1940

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End date: 2019Publisher: search.filters.publisher.Elsevier

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  • Article
    Citation - Scopus: 1
    On the Distribution of the Threshold Voltage in Multi-Level Cell Flash Memories
    (Elsevier, 2019) Pusane, Ali E; Ashrafi, Reza A; Arslan, Şuayb Şefik
    In Multi-Level Cell (MLC) memories, multiple bits of information are packed within the cell to enable higher capacity and lower cost of manufacturing compared to those of the single-level cell flash. However, because of heavy information packing, MLC memories suffer from several error sources including inter-cell interference, retention error, and random telegraph noise which make their lifetime shorter. Having so many error sources that are statistically hard to characterize makes it challenging to properly derive the underlying probability distribution of the sensed threshold voltage, which is vital for finding optimal decision rules to secure better detection performance and hence better lifetime. Although several recent works have already considered this problem, they mostly recourse to few loose assumptions that are far from being realistic. In this study, a more comprehensive/general analysis is conducted to derive the probability density function of the final sensed voltage, and through realistic simplifications, closed form expressions are presented. Extensive computer simulations corroborate the accuracy of the derived analytical expressions, and we think they shall be essential for accurately estimating the reliability and the overall lifetime of modern MLC memories.
  • Book Part
    Citation - Scopus: 3
    The Use of Neurometric and Biometric Research Methods in Understanding the User Experience of First-Time Buyers in E-Commerce - Book Chapter 94
    (Elsevier, 2018) Çakar, Tuna; Öztürk, Özgürol; Rızvanoğlu, Kerem; Çelik, Deniz Zengin
    User experience (UX) research has attracted increasing attention especially in the last decade as the demand for online shopping has increased by 30.7% from 2014 to 2015 in Turkey. The traditional methods including surveys/questionnaires, think-aloud procedures, and in-depth interviews have contributed greatly for understanding the problems during the use of shopping internet sites. On the other hand, the use of neuroscientific methods, such as biometrics and neurometrics, has also grabbed attention with the exciting idea of providing an objective means of understanding cognitive and affective processes during the user experience during online shopping. Despite significant/strong limitations, many researchers are interested in exploring actively its potential use in the field.
  • Article
    Citation - WoS: 13
    Citation - Scopus: 16
    Face Recognition With Patch-Based Local Walsh Transform
    (Elsevier, 2018) Uzun-Per, Meryem; Gökmen, Muhittin
    In this paper, we present a novel dense local image representation method called Local Walsh Transform (LWT)by applying the well-known Walsh Transform (WT) to each pixel of an image. The LWT decomposes an image into multiple components, and produces LWT complex images by using the symmetrical relationship between them. Cascaded LWT (CLWT) is also a dense local image representation obtained by applying the LWT again to real and imaginary parts of LWT complex images. Applying the LWT once more to real and imaginary parts of LWT complex images increases the success rate especially on low resolution images. In order to combine the advantages of sparse and dense local image representations, we present Patch-based LWT (PLWT) and Patch-based CLWT (PCLWT) by applying the LWT and CLWT, respectively, to patches extracted around landmarks of multi-scaled face images. The extracted high dimensional features of the patches are reduced through the application of the Whitened Principal Component Analysis (WPCA). Experimental results show that both thePLWT and PCLWT are robust to illumination and expression changes, occlusion and low resolution. The state-of-the-art performance is achieved on the FERET and SCface databases, and the second best unsupervised category result is achieved on the LFW database.