Yazar "Sansal, Firat" seçeneğine göre listele

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    DETECTING LATERIZATION OF HAEMODYNAMIC RESPONSE DURING EXECUTIVE MOTOR TASK AND MOTOR IMAGERY WITH FNIRS
    (IEEE, 2014) Gokdag, Yunus Engin; Sansal, Firat; Dumlu, Seda Nilgun; Erdogan, Sinem Burcu; Yilmaz, Ozge; Akin, Ata
    In this study, haemodynamic response strength during motor imagery and executive motor tasks are investigated through a general linear model using functional near infrared spectroscopy (fNIRS) data to discriminate neural correlation of right and left hand movement. A 16-channel fNIRS system is used over the prefrontal cortex during motor imagery and motor execution. Preliminary results shows that the activation of the prefrontal cortex during motor imagery task is related with high levels of cognitive processing, namely attentional engagement, rather than motor execution from the measurements using generalised linear model. Results show that fNIRS holds great promise as a tool for clinical studies, cognitive and behavioural neuroscience research.
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    Frontal Brain Activation During A Go/NoGo Response Inhibition Task: An fNIRS Study
    (IEEE, 2014) Sansal, Firat; Gokdag, Yunus Engin; Sahin, Duygu; Keskin, Yasemin; Yilmaz, Ozge; Akin, Ata
    It has been known that in human brain, prefrontal cortex intensively controls the cognitive processes. In this study, functional near infrared spectroscopy (fNIRS) system has been used which is a promising method and has a relatively easier application. The aim of this study is to measure the prefrontal activity of the human brain using a Go-Nogo paradigm which is a common task to measure inhibitory activity in neuroscience field. We have investigated the inhibitory activity triggered by a Go-Nogo paradigm for the first time using fNIRS method with general linear model (GLM) analysis.
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    Image denoising using 2-D wavelet algorithm for Gaussian-corrupted confocal microscopy images
    (Elsevier Sci Ltd, 2019) Gokdag, Yunus Engin; Sansal, Firat; Gokdel, Y. Daghan
    Confocal laser scanning microscopy (CLSM) imaging is a non-invasive optical imaging technique for the examination of the living tissues. CLSM inherently enables in-depth sectioning (z-slices) of the focused specimen. Z-slices of the targeted tissue are gathered by adjusting the focal point on the z-axis into the tissue. Unfortunately, these images can get corrupted with noise of different levels caused by out-of focus light originating from above and below the focal plane. This study proposes a reliable method to indicate and eliminate the additive white Gaussian noise (AWGN) present in real CLSM images of skin tissue. In this work, a denoising algorithm using discrete wavelet transform (DWT) is developed in order to remove the noise by preserving the energy conservation. The effect and performance of different wavelet thresholding algorithms are compared and studied along with different tuning parameters. The selection of components employed in the algorithm affects the noise reduction performance therefore, a systematic approach is presented to obtain and utilize the best combination of these parameter values. Analysis of variance (ANOVA) is exploited to inspect the main and the interaction effects of treated parameters. Computational results show the effectiveness of the methodical tuning approach to CLSM image denoising. (C) 2019 Published by Elsevier Ltd.
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    Implementation of High-Performance LSM Using Wavelet Transformation Analysis
    (IEEE, 2015) Sansal, Firat; Gokdag, Yunus Engin; Kizilcabel, Hilal; Gokdel, Yigit D.
    Laser scanning microscopy (LSM), especially the laser scanning electron microscopy (LSCM) has become a technique which has been frequently used to acquire images of living cells with high resolution and contrast ratio in biomedical researchs including clinical biology. LSCM stands out as one of the most powerful microscopy techniques compared to conventional microscopy methods due to its capability of acquiring slices in the 3rd dimension by solely imaging the cell layers that lies only on focal plane. This study aims to increase the resolution of images by reducing the SNR of data which is obtained from resolution target by established confocal microscopy setup and subsequently using wavelet transform analysis.