Publications by authors named "Ege Cetintas"
Classification of an object behind a random and unknown scattering medium sets a challenging task for computational imaging and machine vision fields. Recent deep learning-based approaches demonstrated the classification of objects using diffuser-distorted patterns collected by an image sensor. These methods demand relatively large-scale computing using deep neural networks running on digital computers.
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- The past decade has seen a surge in electronic cigarette use, raising concerns about the health effects of secondhand exposure to e-cig particles.
- This study utilized a portable device called c-Air, enhanced by deep learning and holographic microscopy, to measure the volatility of exhaled e-cig aerosols in a vape shop over four days.
- Findings indicated that indoor vaping significantly increased the presence of volatile and semi-volatile particles in the air, suggesting a need for more research on the health impacts of these emissions.
Optical coherence tomography (OCT) is a widely used non-invasive biomedical imaging modality that can rapidly provide volumetric images of samples. Here, we present a deep learning-based image reconstruction framework that can generate swept-source OCT (SS-OCT) images using undersampled spectral data, without any spatial aliasing artifacts. This neural network-based image reconstruction does not require any hardware changes to the optical setup and can be easily integrated with existing swept-source or spectral-domain OCT systems to reduce the amount of raw spectral data to be acquired.
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- The study addresses the health risks associated with volatile aerosols, particularly those generated by electronic cigarettes (e-cigs), and introduces a new method for measuring the volatility of particulate matter (PM) using computational microscopy and deep learning.
- The research involves analyzing aerosols created from various e-liquid compositions, revealing that a higher vegetable glycerin (VG) ratio negatively impacts particle volatility, and nicotine influences evaporation dynamics significantly.
- The findings suggest that flavoring agents in e-liquids also lower aerosol volatility, and the new measurement technique could enhance understanding of volatile particles in e-cigs and other sources of particulate matter.