Elektrik - Elektronik Mühendisliği Bölümü Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/202

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    Ball Lens Based Mobile Microscope
    (Gazi Univ, 2016) Icoz, Kutay; 0000-0002-0947-6166; AGÜ, Mühendislik Fakültesi, Elektrik - Elektronik Mühendisliği Bölümü; Icoz, Kutay; 01. Abdullah Gül University
    In this paper we report a low cost, simple and mobile microscope based on attachment of a ball lens to a cell phone. The system's noise and parameters affecting the image quality is investigated. The ball lens provides approximately 100X magnification and together with the cell phone's integrated lens and image sensor, 3,4-micron resolution is reached. The field-of-view of the system is 1500x1500 mu m where the price of the ball lens and the holder is less than 10 cents. By using this system as an optical light microscope, we are able to acquire images of micro particles and micro sensors. When combined with image processing methods, this optical system is capable of doing complex analysis as an alternative to commercial optical light microscopes.
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    Citation - Scopus: 5
    Emotion Detection Using Multivariate Synchrosqueezing Transform via 2D Circumplex Model
    (Institute of Electrical and Electronics Engineers Inc., 2018) Ozel, Pinar; Akan, Aydin; Yilmaz, Bulent; Özel, Pınar; Akan, Aydin I.; Yilmaz, Bulent; AGÜ, Mühendislik Fakültesi, Elektrik - Elektronik Mühendisliği Bölümü; 01. Abdullah Gül University
    Emotion detection by utilizing signal processing methods is a challenging area. An open issue in emotional modeling is to obtain an optimum feature set to use for the classification process. This study proposes an approach for emotional state classification by the investigation of EEG signals via multivariate synchrosqueezing transform (MSST). MSST is a post-processing technique to compose a localized time-frequency representation yielding multivariate syncyrosqueezing coefficients. After obtaining these coefficients from EEG signals for 18 subjects from DEAP dataset, coefficients and self-assessment-mannequins (SAM) labels of those subjects are used for emotional state classification by using support vector machines (SVM) nearest neighbor, decision tree, and ensemble methods. The accuracy rate is 70.6% for high valence high arousal (HVHA), 75.4% for low valence high arousal (LVHA), 77.8% for high valence low arousal (HVLA), and 77.2% for low valence low arousal (LVLA) cases using SVM. © 2019 Elsevier B.V., All rights reserved.