Prof. Darko Zibar Profile

Prof. Darko Zibar

Professor at DTU Electro

SPIE Involvement:

Author

Proceedings Article | 18 June 2024 Poster + Paper

Physics-informed machine learning for programmable photonic circuits

Isidora Teofilovic, Darko Zibar, Francesco Da Ros

Proceedings Volume 13017, 1301711 (2024) https://doi.org/10.1117/12.3017656

KEYWORDS: Data modeling, Physics, Photonic integrated circuits, Machine learning, Analytic models, Integrated photonics

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Proceedings Article | 18 June 2024 Presentation

Machine learning techniques for inverse system design and control of photonic systems

Darko Zibar, Francesco Da Ros

Proceedings Volume PC13017, PC1301701 (2024) https://doi.org/10.1117/12.3017112

KEYWORDS: Machine learning, Control systems, Photonics systems, Design and modelling, Control systems design, Frequency combs, Telecommunications, Raman amplifiers, Optical circuits, Neural networks

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Proceedings Article | 28 September 2023 Presentation + Paper

Machine learning meets photonics

Francesco Da Ros, Metodi Yankov, Darko Zibar

Proceedings Volume 12655, 126550C (2023) https://doi.org/10.1117/12.2676656

KEYWORDS: Machine learning, Data modeling, Photonics, Education and training, Modeling, Systems modeling, Telecommunications, Optical amplifiers, Optical communications, Mathematical optimization

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Proceedings Article | 5 March 2021 Presentation + Paper

Machine-learning-based equalization for short-reach transmission: neural networks and reservoir computing

F. Da Ros, S. Ranzini, R. Dischler, A. Cem, V. Aref, H. Bülow, D. Zibar

Proceedings Volume 11712, 1171205 (2021) https://doi.org/10.1117/12.2583011

KEYWORDS: Neural networks, Signal detection, Data centers, Signal processing, Receivers, Optoelectronics, Numerical analysis, Data communications, Computer architecture

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Proceedings Article | 7 February 2015 Paper

Digital signal processing approaches for semiconductor phase noise tolerant coherent transmission systems

Miguel Iglesias Olmedo, Xiaodan Pang, Richard Schatz, Darko Zibar, Idelfonso Tafur Monroy, Gunnar Jacobsen, Sergei Popov

Proceedings Volume 9388, 93880A (2015) https://doi.org/10.1117/12.2078408

KEYWORDS: Digital signal processing, Semiconductor lasers, Phase shift keying, Semiconductors, Receivers, Interference (communication), Signal to noise ratio, Frequency modulation, Transmitters, Optical communications

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Proceedings Article | 7 February 2015 Paper

Laser characterization with advanced digital signal processing

Molly Piels, Idelfonso Tafur Monroy, Darko Zibar

Proceedings Volume 9388, 938809 (2015) https://doi.org/10.1117/12.2078335

KEYWORDS: Signal to noise ratio, Fermium, Frequency modulation, Machine learning, Digital signal processing, Error analysis, Particle filters, Receivers, Laser beam characterization, Detection and tracking algorithms

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SPIE Journal Paper | 6 April 2012

Experimental characterization of a hybrid fiber-wireless transmission link in the 75 to 110 GHz band

Xiaodan Pang, Xianbin Yu, Darko Zibar, Idelfonso Tafur-Monroy, Ying Zhao, Lei Deng

OE, Vol. 51, Issue 4, 045004, (April 2012) https://doi.org/10.1117/12.10.1117/1.OE.51.4.045004

KEYWORDS: Antennas, Digital signal processing, Amplitude shift keying, K band, Optical amplifiers, Receivers, Interference (communication), Signal attenuation, Wireless communications, Optical engineering

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Proceedings Article | 10 December 2011 Paper

Seamless translation of optical fiber PolMux-OFDM into a 2x2 MIMO wireless transmission enabled by digital training-based fiber-wireless channel estimation

Xiaodan Pang, Ying Zhao, Lei Deng, M. Othman, Xianbin Yu, J. Jensen, D. Zibar, I. Monroy

Proceedings Volume 8309, 83090C (2011) https://doi.org/10.1117/12.905495

KEYWORDS: Orthogonal frequency division multiplexing, Polarization, Single mode fibers, Optical fibers, Radio over Fiber, Digital signal processing, Receivers, Computing systems, Multiplexing, Modulation

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Showing 5 of 8 publications

Conference Committee Involvement (2)

AI and Optical Data Sciences

4 February 2020 | San Francisco, California, United States

Optical Data Science II

6 February 2019 | San Francisco, California, United States

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