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Miljko Bobrek

Researcher at Oak Ridge National Laboratory

Publications -  20
Citations -  338

Miljko Bobrek is an academic researcher from Oak Ridge National Laboratory. The author has contributed to research in topics: Quantum channel & Quantum cryptography. The author has an hindex of 5, co-authored 20 publications receiving 282 citations.

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Generating the Local Oscillator “Locally” in Continuous-Variable Quantum Key Distribution Based on Coherent Detection

TL;DR: In this paper, the authors proposed and demonstrated a pilot-aided feedforward data recovery scheme which enables reliable coherent detection using a locally generated oscillator (LO) for continuous-variable quantum key distribution (CV-QKD).
Journal ArticleDOI

Generating the local oscillator "locally" in continuous-variable quantum key distribution based on coherent detection

TL;DR: A pilot-aided feedforward data recovery scheme which enables reliable coherent detection using a "locally" generated LO and the variance of the phase noise introduced by the proposed scheme is measured to be 0.04, which is small enough to enable secure key distribution.
Journal ArticleDOI

Implantable sensor for blood flow monitoring after transplant surgery.

TL;DR: The ideal characteristics for a perfusion monitoring system are discussed and the development of a new, completely implanted local tissue monitoring system is summarized, which is a photonics-based sensor system uniquely suited for continuous tissue monitoring and real-time data reporting.
Proceedings ArticleDOI

Development of an implantable oximetry-based organ perfusion sensor

TL;DR: In this paper, the present status of system miniaturization is summarized along with plans for future miniaturized efforts.

Enhancing network security using 'learning-from-signals' and fractional fourier transform based rf-dna fingerprints

TL;DR: Early results on the identification of OFDM-based 802.11a WiFi devices using RF Distinct Native Attribute (RF-DNA) fingerprints produced by the Fractional Fourier Transform (FRFT) demonstrate significant improvement over results based on Time Domain, Spectral Domain, and even Wavelet Domain fingerprints.