09/27/2017 - 15:30
Sezer Ulukaya
PhD Thesis Defense

Pulmonary diseases affect the quality of life and disturb the patients throughout their life. Due to some disadvantages of auscultation with a traditional stethoscope, computerized lung sound analysis has become a necessity. In this thesis, novel non-dyadic overcomplete wavelet based methods are proposed to decompose, detect and classify primary indicators (crackle and wheeze) of pulmonary diseases using various machine learning algorithms. Crackle (explosive and discontinuous), wheeze (musical and continuous) and normal lung sounds are classified using Rational Dilation Wavelet Transform based extracted features and compared with related works. It is shown that the proposed method is more successful and faster than its competitors. Moreover, in an ensemble learning scheme it is shown that the optimal representations of signal of interest can be achieved employing the proposed method. Resonance based decomposition using Tunable Q-factor Wavelet Transform and Morphological Component Analysis techniques is proposed to decompose adventitious lung sounds and to localize crackles successfully. The proposed method is compared with related works on adventitious lung sound decomposition and is shown to perform better than other methods in terms of root mean square error, crackle localization accuracy and visual validation. Within class problem in wheeze type classification is explored using non-dyadic wavelet based features and adaptive peak energy ratio metric. It is shown that either using fixed parameter settings in wavelet transform or fixed time-frequency (TF) based features, the optimum representation and high performance can not be achieved. After repetitive experiments, it is shown that by using the proposed novel wavelet based methods, optimum and better TF and time-scale representation can be achieved.


Sezer Ulukaya received the B.Sc. degree in Electronics Engineering from Ankara University and the M.Sc. degree in Electrical and Electronics Engineering from Bahçeşehir University in 2008 and 2011, respectively. He is currently a Ph.D. candidate in the Electrical and Electronics Engineering Department at Boğaziçi University. His research interests are in pattern recognition, machine learning and signal processing with applications to facial biometrics and lung acoustics.



09/20/2017 - 11:00
Bedri Gürkan Sönmez
Ph.D. Thesis Defense
This thesis covers realization and modeling of novel water-gated field effect transistors (WG-FETs) which use 16-nm-thick single crystalline silicon film as active layer. WG-FET devices utilize electrical double layer (EDL) structure as a replacement of gate insulator and operate in the non-Faradaic region (under 1 V) without causing any oxidation/reduction reactions. Performance parameters based on voltage distribution on EDL are extracted and current-voltage relations are modeled. Various WG-FET devices with both probe- and planar-gate setups are simulated, fabricated and tested. Effects of gate distance, gate topology, field and source/drain electrode insulation on transistor performance are investigated. Best ON/OFF ratios are measured with probe-gate devices for both insulated and uninsulated source/drain electrodes. Performance of probe-gate devices with uninsulated source/drain electrodes are superior to the ones with insulated source/drain due to absence of parasitic resistances related with the overlapping area of insulation layer. Planar-gate devices with source/drain insulation have lower ON/OFF ratios compared to probe-gate counterparts and device performance tends to deteriorate with increasing gate distance. Without source/drain electrode insulation, proper transistor operation is not obtained with planar-gate devices. Measurement results are in agreement with theoretical models. Inverters and ring oscillators are realized as circuit applications. Also, a sensing application is implemented as proof of concept. WG-FET is a promising device platform for microfluidic applications where sensors and read-out circuits can be integrated at transistor level.
Bedri Gürkan SÖNMEZ received his B.Sc. degrees in Physics and in Electrical & Electronics Engineering in 2009, and M.Ss. degree in Electrical & Electronics Engineering in 2011 from Bogazici University, Istanbul, Turkey. His M.Sc. studies were about liquid state diodes using polymer semiconductors. He is currently pursuing the Ph.D. degree in microfluidic electronics and circuit applications.
09/25/2017 - 15:00
Assoc. Prof. Dr. Serap Aydın
Beykent University, Biomedical Eng. Dept.

First action potential will be defined as neural level origin of electro-physiological activities in human body, then characteristic properties of AP sums will be described.

Electrophysiological measurement principle and meaning of electrode placement on scalp surface will be shown with respect to analysis approaches in brain biophysics. The relations between experimental data paradigms and specified research goals will be briefly stated to present diversity in electrical brain functions.

New trends, so called neuro-feedback and biofeedback in drug-free treatment will be introduced and finally, forthcoming research topics will be given to offer kindly collaboration.


Serap AYDIN received her B.Sc. degree from the Electrical and Electronics Engineering Department of Gazi University, and M.Sc. degree from the Ondokuz Mayıs Univ. (OMU). Her M.Sc. studies were 2-dimensional discrete-time systems and their modeling approaches in simulations.In 2006, she received her Ph.D. degree from   Middle East Technical Unv. (METU) in the field of biomedical signal processing and with the focus on electrical brain potentials in response to auditory stimuli in healthy young adults. Her active research interests are quantitative bio-markers, complexity metrics, coherence analysis, brain connectivity, neuro-muscular synchronization, statistical analysis and neural-networks.

06/14/2017 - 11:00
Mustafa Altun
Assistant Professor

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