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Apostolos Argyris

Researcher at Spanish National Research Council

Publications -  84
Citations -  3233

Apostolos Argyris is an academic researcher from Spanish National Research Council. The author has contributed to research in topics: Chaotic & Optical communication. The author has an hindex of 22, co-authored 77 publications receiving 2823 citations. Previous affiliations of Apostolos Argyris include National and Kapodistrian University of Athens & Athens State University.

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Chaos-based communications at high bit rates using commercial fibre-optic links

TL;DR: High-speed long-distance communication based on chaos synchronization over a commercial fibre-optic channel is demonstrated, showing that information can be transmitted at high bit rates using deterministic chaos in a manner that is robust to perturbations and channel disturbances unavoidable under real-world conditions.
Proceedings ArticleDOI

Chaos-based communications at high bit rates using commercial fibre-optic links

TL;DR: A thorough study of an all-optical chaotic communication system, including experimental realization real-world testing and performance characterization through bit-error-rate analysis, showed that chaotic communication systems does not act as a considerably deteriorating factor in the final performance.
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Implementation of 140 Gb/s true random bit generator based on a chaotic photonic integrated circuit.

TL;DR: A photonic integrated circuit that emits broadband chaotic signals is employed for ultra-fast generation of true random bit sequences, and the extreme robustness of the random bit generator for adaptive bit-rate operation and for various operating conditions of the PIC is demonstrated.
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Photonic Integrated Device for Chaos Applications in Communications

TL;DR: A novel photonic monolithic integrated device consisting of a distributed feedback laser, a passive resonator, and active elements that control the optical feedback properties has been designed, fabricated, and evaluated as a compact potential chaotic emitter in optical communications.
Journal ArticleDOI

Photonic machine learning implementation for signal recovery in optical communications.

TL;DR: A simplified photonic reservoir computing scheme for data classification of severely distorted optical communication signals after extended fibre transmission is introduced, which demonstrates an improvement in bit-error-rate by two orders of magnitude compared to directly classifying the transmitted signal.