Multi-core fiber (MCF) has attracted increasing attention for application in distributed fiber sensing owing to its unique properties of independent light transmission in multiple spatial channels. Here, we report a distributed acoustic sensing (DAS) system integrated MCF to suppress coherent fading, which overcomes an inevitable challenge in DAS systems. Because the parallel spatial cores in MCF allow the use of space-division multiplexed (SDM) technology, we propose that fading can be effectively suppressed by merging different signals with the spatial rotated-vector-average (SRVA) method. We theoretically analyze the principle of SRVA in fading suppression, and identify that it can effectively reduce phase noise with preventing phase unwrapping failures. In our experiment, a DAS system with 2.58-km length MCF have been investigated, the fading rate of Rayleigh backscattered signals is effectively reduced by three orders of magnitude and the amplitude fluctuation range is decreased by 21.9 dB. Compared with the conventional spectrum extraction and remix method (SERM), SRVA reduces the noise level by 9.5 dB, which also shows excellent low-frequency signal recovery ability. Benefiting from its fading suppression, the false alarm of localization is mitigated and the phase recovery can be distortionless. The proposed and verified method is helpful for the application of SDM-based MCF in long-distance distributed fiber sensors and accelerates the progress of integrated sensing and communication.

Distributed acoustic sensing (DAS) is an attractive technology that can turn existing fibre optic networks to real-time distributed vibration sensors. However, it encounters great challenges in handling the transmission, storage, and processing of voluminous data streams which are orders of magnitude larger than that collected from point sensors. The gap between intensive data generated by φ-OTDR and modern computing system with limited reading/writing speed and storage capacity leads to restrictions on many applications. Compressive sensing (CS) is a revolutionary signal acquisition method that allows a signal to be acquired and reconstructed with significantly fewer samples than that required by Nyquist-Shannon theorem. The data size is greatly reduced in the sampling stage, but the reconstruction of the compressed data is still time and computation consuming. To address this challenge, we propose to map the feature extractor from Nyquist-domain to compressed-domain and therefore vibration detection and classification can be directly implemented in compressed-domain. The measured results show that our method can be used to reduce the transmitted data size by 70% while achieves a true positive rate (TPR) of 99.4% and a false positive rate (FPR) of 0.04% of positioning along 5-km-long sensing fibre and a classification accuracy of 95.05% on a 5-class classification task.

Phase fading and noise are critical issues that hinder phase recovery and location in phase-sensitive optical time-domain reflectometry (ϕ-OTDR) based on distributed acoustic sensing (DAS) systems. This paper introduces a novel approach for signal-to-noise ratio (SNR) enhancement and fading suppression by proposing a Dual-Domain SNR Enhancer (DDSE) method. This method simultaneously processes Rayleigh backscattered (RBS) signals in both the digital and image domains, achieving fading suppression and further noise reduction. In the digital domain, it rotates the complex vector signal and suppresses fading through the moving average in each signal trace. In the image domain, we design and optimize a Discrete Cosine Transform Non-local mean (DCT-NLM) image processing algorithm based on the characteristics of OTDR signals, achieving distortion-free signal recovery and noise reduction. We verify the principle and mechanism of this method both theoretically and experimentally on a ϕ-OTDR system with a 6 km optical fiber. The DDSE method achieved a 26.68 dB reduction in phase noise level and a 14.8 dB enhancement in signal recovery SNR. The method effectively achieves noise reduction and signal enhancement through experimental validation, demonstrating its practicality, reliability, and potential to enhance the performance of ϕ-OTDR systems.

Fiber-based distributed acoustic sensing (DAS) technique has recently captured great attention of academia and industry. The fiber under test (FUT) gains extreme importance for DAS system. However, it is difficult for the commercially available telecom fibers to equip DAS system with long distance, high sensitivity and full distribution. In this work, we have proposed and demonstrated an air-ring microstructure optical fiber (AR-MOF) for fully distributed wideband acoustic sensing with high sensitivity for the first time, to our best knowledge. The cladding of the AR-MOF is constructed by an air-ring for enhancing acousto-optic coupling, and a large mode-area silica core is explored for enlarging the backscattering capture factor. By integrating a 2.2-km AR-MOF into a phase-sensitive optical time-domain reflectometry (Φ-OTDR) system, we have achieved high-sensitivity acoustic detection along the entire FUT within the frequency range from 50 Hz to 5000 Hz. The DAS system shows a maximum acoustic sensitivity of −92.5 dB re rad/μPa at 50 Hz, and an average acoustic sensitivity of −129.4 dB re rad/μPa at the flat area from 500 Hz to 5000 Hz. Compared with previous reports, our AR-MOF based DAS system shows the best sound pressure level sensitivity in such a wide bandwidth. Benefiting from the technical maturity of stack-drawing method and avoidance of nanoparticle doping, the AR-MOF allows for highly sensitive and long-distance DAS system with low attenuation, showing great potential in smart grid, protecting gas pipelines, monitoring of a railway tunnel, illuminating earth phenomenon, real-time intrusion threat detection, marine geophysics, and etc.

Ultrashort pulses experience random quantum motion as they propagate through a mode-locked laser cavity, a phenomenon that inevitably affects the recently introduced pure-quartic solitons. Investigating this process is essential, as quantum-limited noise establishes fundamental performance limits for their application. To date, studies on quantum diffusion and the resulting timing jitter of these solitons remain sparse. In this work, we examine the quantum motion of pure-quartic solitons by injecting amplified spontaneous emission noise into the gain medium, measuring their relative position, and performing analyses using Allan variance and timing jitter power spectral density. Our simulation findings reveal that long-range interactions due to pulse tailing can lead to high-frequency noise, which can be effectively filtered out. In the near-zero dispersion regime, we achieve quantum-limited noise levels significantly less than 1 fs. Good agreement is observed between the analytical model and the simulation results. This work provides insights into the noise levels and mechanisms of pure-quartic solitons and can be extended to more complex scenarios, such as pure-quartic Kerr solitons, and studies on the dynamics of higher-order dispersion solitons.

Carcinoembryonic antigen (CEACAM5), as a broad-spectrum tumor biomarker, plays a crucial role in analyzing the therapeutic efficacy and progression of cancer. Herein, we propose a novel biosensor based on specklegrams of tapered multimode fiber (MMF) and two-dimensional convolutional neural networks (2D-CNNs) for the detection of CEACAM5. The microfiber is modified with CEA antibodies to specifically recognize antigens. The biosensor utilizes the interference effect of tapered MMF to generate highly sensitive specklegrams in response to different CEACAM5 concentrations. A zero mean normalized cross-correlation (ZNCC) function is explored to calculate the image matching degree of the specklegrams. Profiting from the extremely high detection limit of the speckle sensor, variations in the specklegrams of antibody concentrations from 1 to 1000 ng/mL are measured in the experiment. The surface sensitivity of the biosensor is 0.0012 (ng/mL)⁻¹ within a range of 1 to 50 ng/mL. Moreover, a 2D-CNN was introduced to solve the problem of nonlinear detection surface sensitivity variation in a large dynamic range, and in the search for image features to improve evaluation accuracy, achieving more accurate CEACAM5 monitoring, with a maximum detection error of 0.358%. The proposed fiber specklegram biosensing scheme is easy to implement and has great potential in analyzing the postoperative condition of patients.