Optical Fiber Technology最新文献

A noise reduction method based on the combination of improved complete ensemble empirical mode decomposition with adaptive noise and temporal local generalized maximum cross-correlation entropy and wavelet threshold (ICEEMDAN-TLGMCC-WT) is proposed to improve the noise rejection ratio (NRR) of phase-sensitive optical time-domain reflectometer ($\phi$-OTDR) system signals. Firstly, the principle of the proposed ICEEMDAN-TLGMCC-WT denoising method is introduced. On this basis, experiments are carried out to verify the feasibility of the noise reduction algorithm for non-stationary signals. and the results demonstrate that the vibration event can be detected with an improved average NRR of 72.18 dB and a root mean square error (RMSE) 0.017 within 5 km sensing distance by employing the ICEEMDAN-TLGMCC-WT denoising method, which confirm the effectiveness of proposed scheme for the performance improvement of $\phi$-OTDR system.

As a form of multi-carrier modulation, universal filter multicarrier (UFMC) combined with optical communication systems provides significant advantages for long-distance transmission, including large bandwidth, low attenuation, and flexible spectrum resource allocation. While polarization division multiplexing (PDM) enhances system capacity, it concurrently introduces various interference leading to signal distortion. Notably, phase noise has a profound impact on the performance of multi-carrier modulation systems. Phase noise results in stochastic variations to the signal, making the recovery of receiver signals challenging. In this paper, we propose a phase noise suppression method based on Gaussian Basis Expansion (GBE), specifically designed for PDM coherent optical universal filter multicarrier (CO-UFMC) system. We analyze the fundamental principles of phase noise suppression using GBE and validate its effectiveness in optical UFMC transmission systems. In comparison to traditional phase noise suppression methods, our approach exhibits superior phase noise robustness, while maintaining comparable computational complexity. The GBE method significantly enhances the system’s tolerance to laser phase noise and improves its robustness against phase noise. The theoretical analysis is corroborated through numerical simulations of a 16-quadrature amplitude modulation (QAM) optical UFMC system.

A compact sensor based on fiber Fabry-Pérot (FP) cavities dual-cavity matching technique cascaded with fiber Bragg grating (FBG) is proposed and demonstrated to measure pressure and temperature variations inside battery. The sensitivity amplification mechanism for two-parameter measurements is analyzed and a cased reference chamber scheme is designed. Low-cost, small-size, high-temperature and corrosion-resistant all-fiber-optic high sensitivity gas pressure sensors targeting the interior of energy storage batteries are fabricated and cascaded with FBGs to measure temperature parameters. The experimental results show that the sensor has a sensitivity of −26.8322 nm/MPa in a pressure range of 1.86 MPa, the gas pressure inside the battery and the temperature inside and outside the battery at different discharge rates of 1500mAH capacity monobloc batteries can be measured practically, directly and instantly. It has potential applications in detecting subtle pressure variations caused by gas precipitation inside the battery and in temperature measurement, and provides a new concept and a feasible technological path for the measurement of key parameters inside lithium-ion batteries.

Understanding the transmission of light waves in optical fibers and accurately determining the locations of mode coupling are crucial for enhancing the efficiency of optical devices and advancing innovative technologies such as fiber optic sensors, lasers, and modulators. This study utilizes deep learning and image recognition techniques to identify the wavelengths at which mode coupling occurs in optical fibers. Our research findings show that using the ResNet-18 model allows for the rapid and accurate identification of the wavelengths at which mode coupling occurs in optical fibers, as well as the modes involved, achieving an accuracy close to 100 %. We experimented with sampling the dataset at 5 nm and 10 nm intervals to create smaller training and validation sets. Despite the reduced data volume, high accuracy rates were maintained, exceeding 99 % and 97 % respectively. This study provides new insights into the use of deep learning for precise localization of mode coupling points and tracking of transmission modes in optical fibers.

This study introduces a high-resolution optical fiber underwater acoustic sensor utilizing a Fabry-Perot (FP) interferometer design. The sensor consists of a single-mode fiber (SMF) integrated with polydimethylsiloxane (PDMS), forming an FP cavity at the coated SMF terminus. The exceptional elasticity of PDMS amplifies vibrations from acoustic stimuli, modulating the FP cavity for effective acoustic wave detection. This integration of the probe within a single fiber, along with the PDMS coating technique, enhances the sensor’s SNR, resolution, and frequency response. The sensor’s simplicity, cost-effectiveness, superior signal-to-noise ratio, and low power consumption make it a promising tool for underwater acoustic applications.

Cerium oxide (CeO₂), a rare earth metal oxide belonging to the lanthanide group is a mature engineered nanoparticle that has been developed for various industry applications. The nanoparticle features defects in its lattice structure, which is highly advantageous for bandgap tuning, and is acclaimed for its least cytotoxicity among other metal oxides. In this work, CeO₂ is employed as a saturable absorber (CeO₂-SA) in the generation of an L-band Q-switched erbium-doped fiber laser. Q-switching was attained at 33.33 mW pump power threshold with a central wavelength of 1600.512 nm. Within 33.33 mW to 153.20 mW pump power range, the Q-switched laser obtained pulse repetition rates between 11.00 to 34.48 kHz, and pulse widths between 39.0 to 12.0 µs, with a high maximum pulse energy of 410 nJ. The Q-switched fiber laser remained stable over a 45-minute observation time at 153.20 mW pump power. The room temperature and direct mechanical deposition fabrication technique of the CeO₂-SA in this work demonstrates the extreme simplicity and low cost of the device, which would reduce the overall cost and facilitate a wider technology adoption of pulse laser systems.

In this manuscript, we present the experimental realization of a mode-locked Er3+: ZBLAN (ZrF₂- BaF₂- LaF₃- AlF₃- NaF) fiber laser operating at mid-infrared (mid-IR) 2.8 μm band, achieved through the utilization of Tellurium (Te) as the saturable absorber (SA). An exploration of their nonlinear optical absorption properties at 2.8 μm was undertaken employing a dual-channel detection methodology. Our investigation reveals that the Te-SA prepared exhibits a significant broadband saturable absorption response, thereby confirming its suitability as SA for passive mode-locked fiber laser at 2.8 μm. The peak average output power pulse and energy of the mode-locked Er³⁺: ZBLAN fiber laser are 41.2mW and 1.632nJ, respectively. These measurements were conducted at a repetition rate of 25.25 MHz, accompanied by a signal-to-noise ratio (SNR) of 60 dB at a transmit pump power of 600 mW. This is the first demonstration of a mode-locked fiber laser operating in the 2.8 µm mid infrared band using Te-SA.

An optical fiber sensor based on the principle of intensity modulation is proposed, which is composed of plastic optical fibers and silicone tubes. The displacement measurement is achieved by stretching the optical fibers to cause changes in the angle between the end faces, resulting in changes in the coupling efficiency between the end faces. To address the challenges in the performance calibration of fiber optic respiratory sensors, it is proposed to convert the dynamic characteristics measurement of the respiratory sensor into the static performance analysis of the displacement sensor, so as to obtain the characteristics such as sensitivity, linearity, and repeatability, etc. It is further proposed to use a stepper motor to simulate the periodic stretching process of the optical fibers caused by the respiratory process, enabling the analysis of frequency measurement accuracy under different respiratory strengths. Simulation and experimental results demonstrate that the proposed sensor has high linearity, good repeatability, high sensitivity, and a wide measurement range. It can accurately measure respiratory frequency and display amplitude information, providing comprehensive information for respiratory monitoring.

Distributed fiber optic sensing (DFOS) based on phase-sensitive optical time-domain reflectance (φ-OTDR) technology has outstanding performance in pipeline safety monitoring and perimeter security detection. Accurate identification of new events remains challenging due to environmental variability and emerging forms of intrusions. In order to solve the problem of failing to accurately identify new events due to the inability to obtain all samples at once in real-time monitoring, this paper proposes an incremental learning network framework for distributed fiber-optic sensing signal recognition. This framework integrates an optimized Learning without Memorizing (LwM) algorithm with an improved ConvNeXt network for dynamic training of new events. An improved Efficient Channel Attention (HECA) is incorporated to thoroughly extract the spatio-temporal features of the intrusion signals collected by the DFOS. The forgetting problem is mitigated during incremental learning using knowledge distillation and optimized Gradient Weighted Class Activation Mapping to generate an attention map. A linear correction layer is added after the output layer to correct the bias towards new classes by rebalancing the information between new and old classes. Experimental comparisons show that the recognition rate for 10 different intrusion signals exceeds 93 %, while the forgetting rate is reduced from a peak of 41.44 % to 5.25 %. The time required to process and train incremental learning for 1000 samples in real time on an edge device (NVIDIA 3050 GPU) is approximately 1060 s, its ability to demonstrating its suitability for deployment in resource-constrained.

In our experiments, applying few shot metric learning for optical performance monitoring (OPM), we set the dataset as 16-way-6-shot. Modulation format identification (MFI) was utilized as a classification task, and optical signal-to-noise ratio (OSNR) estimation was used as a regression task for joint analysis. Multi-task metric learning (MML) used the adaptive weights to balance the weights of the three metric functions, six modulation formats (QPSK, 8QAM, 16QAM, 32QAM, 64QAM, 128QAM) are classified correctly with 100 % accuracy after 200 epochs. Furthermore, the lowest mean square error (MSE) of OSNR is 0.431 dB. Then, Ablation experiments compute the corresponding similarity (SIM) for each metric function show that the MSE of MML, SIM_Local+Cosine, SIM_Cosine+Point, SIM_Local+Point, single-task metric learning (SML) and adaptive multi-task learning (AMTL) is 0.431 dB, 0.572 dB, 0.569 dB, 0.567 dB, 0.637 dB, 1.319 dB, respectively. The proposed model achieves the highest accuracy in MFI and the lowest MSE in OSNR estimation. Finally, when comparing the various metric functions while altering the transmission distance of the optical fiber, it was observed that MML stayed within an acceptable range between 200 km and 800 km. This shows that our algorithm requires only a small number of training samples to create a reasonably good model, offering a new approach to solving problems that arise in optical performance monitoring.