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Permanent URI for this collectionhttps://hdl.handle.net/20.500.14411/19

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Browsing Scopus by browse.metadata.publisher "Allied Acad"

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    Determination of Measurement Noise, Conductivity Errors and Electrode Mislocalization Effects To Somatosensory Dipole Localization.
    (Allied Acad, 2012) Sengul, G.; Baysal, U.; Computer Engineering
    Calculating the spatial locations, directions and magnitudes of electrically active sources of human brain by using the measured scalp potentials is known as source localization. An accurate source localization method requires not only EEG data but also the 3-D positions and number of measurement electrodes, the numerical head model of the patient/subject and the conductivities of the layers used in the head model. In this study we computationally determined the effect of noise, conductivity errors and electrode mislocalizations for electrical sources located in somatosensory cortex. We first randomly selected 1000 electric sources in somatosensory cortex, and for these sources we simulated the surface potentials by using average conductivities given in the literature and 3-D positions of the electrodes. We then added random noise to measurements and by using noisy data; we tried to calculate the positions of the dipoles by using different electrode positions or different conductivity values. The estimated electrical sources and original ones are compared and by this way the effect of measurement noise, electrode mislocalizations and conductivity errors to somatosensory dipole localization is investigated. We conclude that for an accurate somatosensory source localization method, we need noiseless measurements, accurate conductivity values of scalp and skull layers and the accurate knowledge of 3-D positions of measurement sensors.
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    Citation - WoS: 8
    Citation - Scopus: 13
    White Blood Cells Classifications by SURF Image Matching, PCA and Dendrogram
    (Allied Acad, 2015) Nazlibilek, Sedat; Karacor, Deniz; Erturk, Korhan Levent; Sengul, Gokhan; Ercan, Tuncay; Aliew, Fuad; Department of Mechatronics Engineering; Information Systems Engineering; Computer Engineering
    Determination and classification of white blood cells are very important for diagnosing many diseases. The number of white blood cells and morphological changes or blasts of them provide valuable information for the positive results of the diseases such as Acute Lymphocytic Leucomia (ALL). Recognition and classification of white cells as basophils, lymphocytes, neutrophils, monocytes and eosinophils also give additional information for the diagnosis of many diseases. We are developing an automatic process for counting, size determination and classification of white blood cells. In this paper, we give the results of the classification process for which we experienced a study with hundreds of images of white blood cells. This process will help to diagnose especially ALL disease in a fast and automatic way. Three methods are used for classification of five types of white blood cells. The first one is a new algorithm utilizing image matching for classification that is called the Speed-Up Robust Feature detector (SURF). The second one is the PCA that gives the advantage of dimension reduction. The third is the classification tree called dendrogram following the PCA. Satisfactory results are obtained by two techniques.