Master Degree / Yüksek Lisans Tezleri

Permanent URI for this collectionhttps://hdl.handle.net/11147/3008

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2004 - 200912010 - 20142

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Author: search.filters.author.Savaci, Ferit AcarHas files: Yes

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  • Master Thesis
    Brain Dynamics and Memory Enhancement: Memristor Based Models
    (Izmir Institute of Technology, 2014) Yücel, Ufuk; Savacı, Ferit Acar; Savaci, Ferit Acar; 03.05. Department of Electrical and Electronics Engineering; 03. Faculty of Engineering; 01. Izmir Institute of Technology
    In this thesis, the memory systems of the brain are researched and the relationship between these memory systems and memristor theory are studied. Firstly, the ideal memristor theory is studied. By using the cubic memristor that was based on the ideal memristor theory, the basic logical operations are explained. Additionally, the nerve cells and synapses, which are thought where the memory and learning take place, are inquired. Bearing in mind that the characteristic of the memristor and its similarities between the synapses has been researched with the previous studies on this field. Finally, memristor data storage system has been designed by using memristors, and a binary image of 12x60 pixels has been successfully stored on this design. Also, the edge detection for images has been presented by using the memristor cellular automaton and some examples have also been given.
  • Master Thesis
    Analysis of Olfactory Evoked Potentials
    (Izmir Institute of Technology, 2014) Olcay, Bilal Orkan; Olcay, Bilal Orkan; Savacı, Ferit Acar; Savaci, Ferit Acar; 01.01. Units Affiliated to the Rectorate; 03.05. Department of Electrical and Electronics Engineering; 01. Izmir Institute of Technology; 03. Faculty of Engineering
    With the growing opportunities of laboratories and measurement techniques, cognitive science attracts many researchers interest from other branches of science. In the literature, lack of studies related to the brain's responsiveness against the olfactory stimuli has been the main source of motivation for our work on this issue. In this thesis, it is examined by means of time-dependent wavelet entropy of Electroencephalographic (EEG) signals which is collected from individuals that how olfactory and trigeminal effective odor stimuli affects responsiveness of the brain. Significance and meaningfulness of the results are shown with statistical tests of average entropy in the discrete time windows. Due to its nature of small amplitude in comparison with ongoing EEG activity, it’s hard to observe the components of olfactory evoked potentials and trigeminal evoked potentials. In order to separate these components from ongoing EEG, different signal processing techniques have been employed in this thesis. And, findings from these techniques have been conveyed to statistical tests to determine the most suitable technique for that purpose. Additionally, a novel smell performance identification metric have been offered for clinical studies that is not affected by basal activity of brain and subjective review, for objective assessment of smell performance. Statistical test result have shown that, results of this technique which is performed on 19 participants, and their TDI scores obtained from Sniffin’ Stick test battery, are in a strong correlation.
  • Master Thesis
    Analysis of Observed Chaotic Data
    (Izmir Institute of Technology, 2004) Çek, Mehmet Emre; Savacı, Ferit Acar; Savaci, Ferit Acar; 03.05. Department of Electrical and Electronics Engineering; 03. Faculty of Engineering; 01. Izmir Institute of Technology
    In this thesis, analysis of observed chaotic data has been investigated. The purpose of analyzing time series is to make a classification between the signals observed from dynamical systems. The classifiers are the invariants related to the dynamics. The correlation dimension has been used as classifier which has been obtained after phase space reconstruction. Therefore, necessary methods to find the phase space parameters which are time delay and the embedding dimension have been offered. Since observed time series practically are contaminated by noise, the invariants of dynamical system can not be reached without noise reduction. The noise reduction has been performed by the new proposed singular value decomposition based rank estimation method.Another classification has been realized by analyzing time-frequency characteristics of the signals. The time-frequency distribution has been investigated by wavelet transform since it supplies flexible time-frequency window. Classification in wavelet domain has been performed by wavelet entropy which is expressed by the sum of relative wavelet energies specified in certain frequency bands. Another wavelet based classification has been done by using the wavelet ridges where the energy is relatively maximum in time-frequency domain. These new proposed analysis methods have been applied to electrical signals taken from healthy human brains and the results have been compared with other studies.