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Ye J, Gao X, Li X, Yang H, An Y, Xu P, Wang A, Dong X, Wang Y, Qin Y, Gao Z. Physical layer security--enhanced optical communication based on chaos masking and chaotic hardware encryption. OPTICS EXPRESS 2024; 32:27734-27747. [PMID: 39538604 DOI: 10.1364/oe.529540] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/07/2024] [Accepted: 07/08/2024] [Indexed: 11/16/2024]
Abstract
The security and confidentiality of information are crucial in contemporary communication systems. In this work, we propose a physical layer security-enhanced optical communication scheme based on dual-level protection with chaos masking (CMS) and chaotic hardware encryption. The integration of CMS and chaotic hardware encryption contributes to enhancing the security of the system. Different uncorrelated chaos generated from a single Fabry-Perot (FP) laser are employed to independently mask and encrypt the confidential signals for multiple channels in a wavelength division multiplexing (WDM) system. Thanks to the CMS and temporal intensity scrambling, the signals are encrypted into a noise-like signal to against direct demasking or decryption attacks. Compared to individual CMS or encrypting the signals using stand-alone dispersion components, numerical results demonstrate that the proposed scheme significantly enhances communication security. The decrypted bit error rate (BER) for 10 Gb/s data in each channel at the legitimate receiver is lower than the hard decision forward error correction threshold (HD-FEC) of 3.8 × 10-3 for a proof-of-principle demonstration. This approach enables multi-path parallel and independent security-enhanced chaotic optical communication, offering a promising solution for high-capacity secure optical communication.
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Gritsenko TV, Orlova MV, Zhirnov AA, Konstantinov YA, Turov AT, Barkov FL, Khan RI, Koshelev KI, Svelto C, Pnev AB. Detection and Recognition of Voice Commands by a Distributed Acoustic Sensor Based on Phase-Sensitive OTDR in the Smart Home Concept. SENSORS (BASEL, SWITZERLAND) 2024; 24:2281. [PMID: 38610492 PMCID: PMC11013987 DOI: 10.3390/s24072281] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/29/2024] [Revised: 03/18/2024] [Accepted: 03/21/2024] [Indexed: 04/14/2024]
Abstract
In recent years, attention to the realization of a distributed fiber-optic microphone for the detection and recognition of the human voice has increased, whereby the most popular schemes are based on φ-OTDR. Many issues related to the selection of optimal system parameters and the recognition of registered signals, however, are still unresolved. In this research, we conducted theoretical studies of these issues based on the φ-OTDR mathematical model and verified them with experiments. We designed an algorithm for fiber sensor signal processing, applied a testing kit, and designed a method for the quantitative evaluation of our obtained results. We also proposed a new setup model for lab tests of φ-OTDR single coordinate sensors, which allows for the quick variation of their parameters. As a result, it was possible to define requirements for the best quality of speech recognition; estimation using the percentage of recognized words yielded a value of 96.3%, and estimation with Levenshtein distance provided a value of 15.
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Affiliation(s)
- Tatyana V. Gritsenko
- Laser and Optoelectronic Systems Department, Radio Electronics and Laser Technology Faculty, Bauman Moscow State Technical University, 2-nd Baumanskaya 5-1, 105005 Moscow, Russia; (T.V.G.); (M.V.O.); (A.A.Z.); (R.I.K.); (K.I.K.)
| | - Maria V. Orlova
- Laser and Optoelectronic Systems Department, Radio Electronics and Laser Technology Faculty, Bauman Moscow State Technical University, 2-nd Baumanskaya 5-1, 105005 Moscow, Russia; (T.V.G.); (M.V.O.); (A.A.Z.); (R.I.K.); (K.I.K.)
| | - Andrey A. Zhirnov
- Laser and Optoelectronic Systems Department, Radio Electronics and Laser Technology Faculty, Bauman Moscow State Technical University, 2-nd Baumanskaya 5-1, 105005 Moscow, Russia; (T.V.G.); (M.V.O.); (A.A.Z.); (R.I.K.); (K.I.K.)
| | - Yuri A. Konstantinov
- Perm Federal Research Center of the Ural Branch of the Russian Academy of Sciences (PFRC UB RAS), 13a Lenina St., 614990 Perm, Russia; (Y.A.K.); (A.T.T.); (F.L.B.)
| | - Artem T. Turov
- Perm Federal Research Center of the Ural Branch of the Russian Academy of Sciences (PFRC UB RAS), 13a Lenina St., 614990 Perm, Russia; (Y.A.K.); (A.T.T.); (F.L.B.)
- General Physics Department, Applied Mathematics and Mechanics Faculty, Perm National Research, Polytechnic University, Prospekt Komsomolsky 29, 614990 Perm, Russia
| | - Fedor L. Barkov
- Perm Federal Research Center of the Ural Branch of the Russian Academy of Sciences (PFRC UB RAS), 13a Lenina St., 614990 Perm, Russia; (Y.A.K.); (A.T.T.); (F.L.B.)
| | - Roman I. Khan
- Laser and Optoelectronic Systems Department, Radio Electronics and Laser Technology Faculty, Bauman Moscow State Technical University, 2-nd Baumanskaya 5-1, 105005 Moscow, Russia; (T.V.G.); (M.V.O.); (A.A.Z.); (R.I.K.); (K.I.K.)
| | - Kirill I. Koshelev
- Laser and Optoelectronic Systems Department, Radio Electronics and Laser Technology Faculty, Bauman Moscow State Technical University, 2-nd Baumanskaya 5-1, 105005 Moscow, Russia; (T.V.G.); (M.V.O.); (A.A.Z.); (R.I.K.); (K.I.K.)
| | - Cesare Svelto
- Dipartimento di Elettronica, Informazione e Bioingegneria Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milano, Italy;
| | - Alexey B. Pnev
- Laser and Optoelectronic Systems Department, Radio Electronics and Laser Technology Faculty, Bauman Moscow State Technical University, 2-nd Baumanskaya 5-1, 105005 Moscow, Russia; (T.V.G.); (M.V.O.); (A.A.Z.); (R.I.K.); (K.I.K.)
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Ali J, Almaiman A, Ragheb AM, Esmail MA, Almohimmah EM, Alshebeili SA. Multievent localization for loop-based Sagnac sensing system using machine learning. OPTICS EXPRESS 2023; 31:24005-24024. [PMID: 37475239 DOI: 10.1364/oe.495367] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Accepted: 06/23/2023] [Indexed: 07/22/2023]
Abstract
In optical sensing applications such as pipeline monitoring and intrusion detection systems, accurate localization of the event is crucial for timely and effective response. This paper experimentally demonstrates multievent localization for long perimeter monitoring using a Sagnac interferometer loop sensor and machine learning techniques. The proposed method considers the multievent localization problem as a multilabel multiclassification problem by dividing the optical fiber into 250 segments. A deep neural network (DNN) model is used to predict the likelihood of event occurrence in each segment and accurately locate the events. The sensing loop comprises 106.245 km of single-mode fiber, equivalent to ∼50 km of effective sensing distance. The training dataset is constructed in simulation using VPItransmissionMaker, and the proposed machine learning model's complexity is reduced by using discrete cosine transform (DCT). The designed DNN is tested for event localization in both simulation and experiment. The simulation results show that the proposed model achieves an accuracy of 99% in predicting the location of one event within one segment error, an accuracy of 95% in predicting the location of one event out of the two within one segment error, and an accuracy of 78% in predicting the location of the two events within one segment error. The experimental results validate the simulation ones, demonstrating the proposed model's effectiveness in accurately localizing events with high precision. In addition, the paper includes a discussion on extending the proposed model to sense more than two events simultaneously.
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