Graduate School of Engineering and Science

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Browsing by Institution Author "DURAK, Kadir"

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    Down-conversion emission profile characterisation via camera
    (Optica Publishing Group, 2020-12-14) Kuniyil, Hashir Puthiyapurayil; Durak, Kadir; Electrical & Electronics Engineering; DURAK, Kadir; Kuniyil, Hashir Puthiyapurayil
    We present a method to improve the brightness and collection efficiency of the spontaneous parametric down-conversion source by monitoring the mode shape using camera and correcting it with collection optics.
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    Photon statistics effects on a QRNG of vacuum fluctuations
    (Optica Publishing Group, 2020-09-14) Dandaşi, Abdulrahman; Özel, Helin; Durak, Kadir; Electrical & Electronics Engineering; DURAK, Kadir; Dandaşi, Abdulrahman; Özel, Helin
    Optical scattering enhances randomness characteristics, increases the chaotic behavior of coherent sources, broadens the distribution of photon statistics and makes it super-Poissonian which allows faster sampling compared to Poissonian.
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    PYNQ-based rapid FPGA implementation of quantum key distribution
    (IEEE, 2021) Bilgin, Yiğit; Tesfay, Shewıt Weldu; İpek, Seçkin; Uğurdağ, Hasan Fatih; Durak, Kadir; Gören, S.; Electrical & Electronics Engineering; UĞURDAĞ, Hasan Fatih; DURAK, Kadir; Bilgin, Yiğit
    In this paper, we present a real-time Quantum Key Distribution (QKD) implementation on Field Programmable Gate Arrays (FPGAs) for secure communication. We propose a novel methodology with a Python-based programming interface for rapid development on FPGA. Our methodology revolves three phases of development. In the first phase, a reference model of an entangled photon source and the proposed QKD system are developed in Python. Next, the reference model is passed through a thorough verification phase. In the second phase, the reference model is implemented on the Processing System (PS) part of the FPGA. Finally in the third phase, the computationally intensive part of the QKD architecture is off-loaded on to the Programmable Logic (PL) part of the FPGA for acceleration. We employ PYNQ framework in our QKD development and successfully combine the convenience of Python productivity with FPGA based acceleration.