Using machine learning to enlarge the measurement range and promote the compactness of the optical fiber torsion sensor based on the Sagnac interferometer.

The support vector regression (SVR) algorithm is presented to demodulate the torsion angle of an optical fiber torsion sensor based on the Sagnac interferometer with the panda fiber. Experimental results demonstrate that with the aid of SVR algorithm, the information in the transmission spectrum of the sensor can be used fully to realize the regression prediction of the directional torsion angle. The full torsion angle ranges from -360° to 360° can be predicted with a mean absolute error (MAE) of 2.24° and determination coefficient (R2) of 0.9996. The impact of the angle sampling interval and wavelength resolution of the spectrometer on the prediction accuracy of the directional torsion angle and the suitability of the SVR algorithm for compact optical fiber sensor and other optical fiber torsion sensors based on the Sagnac interferometer are discussed. Moreover, the multi-objective SVR algorithm is used to eliminate the interference of strain during torsion angle measurement. The SVR algorithm can efficiently enlarge the measurement range of the torsion angle and break through the challenge of demodulating sensing signal for compact fiber torsion sensor. Compared to the prediction accuracy of common machine learning algorithms of artificial neural network (ANN) algorithm, random forest (RF) algorithm, and K-nearest neighbor (KNN) algorithm, the SVR algorithm has the advantages of higher measurement accuracy and shorter testing time.

High sensitivity twist sensor based on suspended core fiber Sagnac interferometer with temperature calibration.

A high sensitivity optical fiber twist sensor based on Suspend Core Fiber Sagnac Interference (SCFSI) is proposed and experimentally demonstrated. By filling the air hole of the Suspend Core Fiber (SCF) with alcohol, the twist sensitivity of the twist sensor is greatly improved to 8.37 nm/°. Moreover, the valid angle measurement range of the sensor can be expanded by utilizing the combination of intensity demodulation and wavelength demodulation. The sensor not only has high twist angle sensitivity but also exhibits a capability of temperature calibration. Since the wavelength shifts of the interference fringes of Mach-Zehnder Interferometer (MZI) formed in the suspend core of SCF appears insensitive to twist angle, the parasitic interference formed by MZI can be used for temperature calibration. Due to compact structure, easy fabrication and low temperature cross sensitivity, the proposed sensor has a great potential for structural health monitoring, such as buildings, towers, bridges, and many other infrastructures.

Machine-learning-assisted omnidirectional bending sensor based on a cascaded asymmetric dual-core PCF sensor.

An omnidirectional bending sensor comprising cascaded asymmetric dual-core photonic crystal fibers (ADCPCFs) is designed and demonstrated experimentally. Upon cascading and splicing two ADCPCFs at a lateral rotation angle, the transmission spectrum of the sensor becomes highly dependent on the bending direction. Machine learning (ML) is employed to predict the curvature and bending orientation of the bending sensor for the first time, to the best of our knowledge. The experimental results demonstrate that the ADCPCF sensor used in combination with machine learning can predict the curvature and omnidirectional bending orientation within 360° without requiring any post-processing fabrication steps. The prediction accuracy is 99.85% with a mean absolute error (MAE) of 2.7° for bending direction measurement and 98.08% with an MAE of 0.03 m-1 for the curvature measurement. This promising strategy utilizes the global features (full spectra) in combination with machine learning to overcome the dependence of the sensor on high-quality transmission spectra, the wavelength range, and a special wavelength dip in the conventional dip tracking method. This excellent omnidirectional bending sensor has large potential for structural health monitoring, robotic arms, medical instruments, and wearable devices.

3D-printed high-birefringence THz hollow-core anti-resonant fiber with an elliptical core.

A high-birefringence and low-loss terahertz (THz) hollow-core anti-resonant fiber (THz HC-ARF) is designed and analyzed numerically by the finite element method (FEM). The THz HC-ARF is composed of an elliptical tube as the core for high birefringence guidance and a pair of symmetrical slabs arranged vertically as the cladding to attain low loss. Numerical analysis indicates that the birefringence reaches 10-2 in the transmission window between 0.21 and 0.35 THz. The highest birefringence is 4.61 × 10-2 at 0.21 THz with a loss of 0.15 cm-1. To verify the theoretical results, the THz HC-ARF is produced by three-dimensional (3D) printing, and the transmission characteristics are determined by THz time-domain spectroscopy (THz-TDS). High birefringence in the range of 2.17 × 10-2 to 3.72 × 10-2 and low loss in the range of 0.12 to 0.18 cm-1 are demonstrated experimentally in the 0.2 to 0.27 THz transmission window. The highest birefringence is 3.72 × 10-2 at 0.22 THz and the corresponding loss is 0.18 cm-1. The THz HC-ARF shows the highest birefringence besides relatively low loss compared to similar THz HC-ARFs reported recently.

Ultra-broadband 3-dB coupler based on dual-hollow-core polymer fiber covering E + S + C + L + U band.

An ultra-broadband 3-dB coupler based on a polymer dual-hollow-core anti-resonant fiber (DHC-ARF) is designed to work in the E + S + C + L + U communication band. By incorporating two elliptical-like cores and modulating the air gap between the two cores, the wavelength and polarization dependence of the DHC-ARF-based coupler is reduced effectively. The feasibility of using a 1.46 cm long DHC-ARF as the ultra-broadband coupler for the operating bandwidth of 400 nm in the range between 1.33 µm and 1.73 µm is demonstrated theoretically. The coupling ratio of each polarized mode stabilizes at 50 ± 2% and the coupling ratio difference between the two polarized modes changes within ±0.6%. This DHC-ARF coupler which is made of a polymer can be fabricated by high-resolution 3D printing. Compared to a silica-based DHC-ARF coupler, the polymer-based DHC-ARF coupler is easier to manufacture and the total loss of the latter is only 0.041 ± 0.006 dB in the operating bandwidth. The polymer hollow-core fiber coupler boasting an ultra-broadband, short component length, and low loss is very promising in next-generation, high-speed, and large-capacity hollow-core fiber communication systems.

Compact single-polarization coupler based on a dual-hollow-core anti-resonant fiber.

A compact single-polarization (SP) coupler based on a dual-hollow-core anti-resonant fiber (DHC-ARF) is proposed. By introducing a pair of thick-wall tubes into a ten-tube single-ring hollow-core anti-resonant fiber, the core is separated into two cores to form the DHC-ARF. More importantly, by introducing the thick-wall tubes, dielectric modes in the thick wall are excited to inhibit the mode-coupling of secondary eigen-state of polarization (ESOP) between two cores while the mode-coupling of the primary ESOP can be enhanced, and thus the coupling length (Lc) of the secondary ESOP is greatly increased and that of primary ESOP is reduced to several millimeters. Simulation results show that the Lc of the secondary ESOP is up to 5549.26 mm and one of the primary ESOP is only 3.12 mm at 1550 nm through optimizing fiber structure parameters. By using a 1.53-mm-long DHC-ARF, a compact SP coupler can be implemented with a polarization extinction ratio (PER) less than - 20 dB within the wavelength range from 1547 nm to 1551.4 nm, and the lowest PER of - 64.12 dB is achieved at 1550 nm. Its coupling ratio (CR) is stable within 50 ± 2% in the wavelength range from 1547.6 nm to 1551.4 nm. The novel compact SP coupler provides a reference for developing HCF-based polarization-dependent components for use in the high-precision miniaturized resonant fiber optic gyroscope.

Single and composite disturbance event recognition based on the DBN-GRU network in φ-OTDR.

An end-to-end deep learning model based on the deep belief network (DBN) and gated recurrent unit (GRU) is proposed to recognize the single disturbance events and composite disturbance events in the phase-sensitive optical time-domain reflectometer (φ-OTDR). Making use of the DBN to fit the original data, five kinds of single disturbance events can be effectively recognized with the GRU network as the classifier. An average recognition accuracy of 96.72% with a short recognition time of 0.079 s can be achieved for single disturbance events. Moreover, the proposed method is also applied for recognizing composite disturbance events. Four kinds of composite disturbance events can be recognized with an average recognition accuracy as high as 90.94%, and the corresponding recognition time is only 0.084 s. Up until now, there have been fewer reports about the recognition of composite disturbance events in φ-OTDR systems. High recognition accuracy and short recognition time make the model based on DBN-GRU more capable in a high sensitivity, real-time φ-OTDR system.

Hybrid structure polarization-maintaining hollow-core photonic bandgap fiber with anti-resonant tubes and silicon layers.

A novel-hybrid structure polarization-maintaining 19-cell hollow-core photonic bandgap fiber (HC-PBGF) is proposed. Robust single-mode characteristic is achieved by introducing six anti-resonant tubes into the core of 19-cell HC-PBGF. A high birefringence at the level of 10-3 is achieved by adding silicon layers into the y-direction tubes. The higher-order mode extinction ratio (HOMER) is greater than 4.71 × 107, and the high birefringence can be improved to 5 × 10-3. In the waveband from 1530 nm to 1595 nm, the single-mode, high birefringence performance can be effectively maintained even under a tight bending radius of 5 mm.

Long short-term memory network of machine learning for compensating temperature error of a fiber optic gyroscope independent of the temperature sensor.

We present an artificial intelligence compensation method for temperature error of a fiber optic gyroscope (FOG). The difference from the existing methods is that the compensation model finally determined by this method only uses the FOG's data to complete the regression prediction of the temperature error and eliminate the dependency on the temperature sensor. In the experimental stage, the proposed method performs temperature experiments with three varying trends of temperature heating, holding, and cooling and obtains sufficient output data sets of the FOG. Taking the output time series of the FOG as the input sample and based on the long short-term memory network of machine learning, the training, validation, and test of the model are completed. From the two perspectives of network learning ability and the improvement degree of the FOG's performance, four indicators, including root mean square error, error cumulative distribution function, FOG bias stability, and Allan variance analysis are selected to evaluate the performance of the compensation model comprehensively. Compared with the existing methods using temperature information for prediction and compensation, the results show that the error compensation method without temperature information proposed can effectively improve the accuracy of the FOG and reduce the complexity of the compensation system. The work can also provide technical references for error compensation of other sensors.

Pattern recognition using self-reference feature extraction for φ-OTDR.

This paper proposes a pattern recognition method for φ-OTDR based on self-reference features, where machine learning is applied to classify the vibration monitored. The φ-OTDR collects the light amplitude-time-space sequence, establishes a reference position in the spatial dimension, and combines the two dimensions of the vibration and reference positions to form self-reference features, which are then used as machine learning features. These self-reference features can effectively improve the pattern recognition accuracy. This paper selects a low sampling frequency for data collection, analyzes the influence of sample definition methods of different time lengths on the pattern recognition accuracy, and determines that the optimal sample length is 10 data points. The contribution of different feature parameters to pattern recognition is analyzed, and eight eigenvalues such as average, maximum, and minimum are finally determined to form self-reference features that are used as the input of the machine learning algorithm. The recognition accuracies of five machine learning algorithms including kNN, Decision Tree, Random Forest, LightGBM, and CatBoost are analyzed and compared, and the CatBoost algorithm in the integrated learning algorithm is finally determined as the optimal algorithm. On this basis, this paper proposes a filtering algorithm to deal with abnormal signals, which can effectively compensate for abnormal data and further improve the accuracy of pattern recognition. Finally, this paper conducts the pattern recognition study on four common events of tapping, bending, trampling, and blowing, and obtains the average recognition rate of 98%. In addition, this paper innovatively carried out pattern recognition research on five types of mining equipment, including ball mills, vibrating screens, conveyor belts, filters, and industrial pumps, and obtained the average recognition rate of 93.5%.

Single-ring hollow-core anti-resonant fiber with a record low loss (4.3†dB/km) for high-power laser delivery at 1†µm.

We report a 7-tube single-ring hollow-core anti-resonant fiber (SR-ARF) with a record low transmission loss of 4.3 dB/km @1080 nm, which is almost half of the current lowest loss record of an SR-ARF (7.7 dB/km @750 nm). The 7-tube SR-ARF has a large core diameter of 43 µm and a wide low-loss transmission window exceeding 270 nm for the 3-dB bandwidth. Moreover, it exhibits an excellent beam quality with an M2 factor of 1.05 after 10-m-long transmission. The robust single-mode operation, ultralow loss, and wide bandwidth make the fiber an ideal candidate for short-distance Yb and Nd:YAG high-power laser delivery.

All-solid anti-resonant single crystal fibers.

In this paper, a novel all-solid anti-resonant single crystal fiber (AR-SCF) with high refractive index tubes cladding is proposed. By producing the cladding tubes with high refractive index material, the AR guiding mechanism can be realized for the SCF, which can reduce the mode number to achieve single-mode or few-mode transmission. The influences of different materials and structures on the confinement loss and effective guided mode number for wavelengths of 2-3 µm are investigated. Then, the optimal AR-SCF structures for different wavelengths are determined. Furthermore, the influences of different fabrication errors are analyzed. This work would provide insight to new opportunities in the novel design of SCFs by AR, which would greatly impact the fields of laser application, supercontinum generation, and SCF sensors.

Ultrawide bandwidth single-mode polarization beam splitter based on dual-hollow-core antiresonant fiber.

An ultrawide bandwidth and single-mode polarization beam splitter (PBS) based on an air-gap type of dual-hollow-core antiresonant fiber (DHC-ARF) is proposed. Nested tubes are introduced into two cladding tubes between two cores to weaken the wavelength dependence of coupling length in DHC-ARF for obtaining ultrawide bandwidth. By tuning the cladding tube sizes, higher-order core modes with the lowest loss can be coupled with cladding tube modes, and thus, effectively, single-mode operation is achieved. Numerical results demonstrate that an 8.15 cm long DHC-ARF can be used to develop a PBS with an operating bandwidth of 370 nm ranging from 1.28 to 1.65 µm, where a polarization extinction ratio is below -20dB and a high-order mode extinction ratio exceeds 100.

Discovering extremely low confinement-loss anti-resonant fibers via swarm intelligence.

In this work, we obtain extremely low confinement-loss (CL) anti-resonant fibers (ARFs) via swarm intelligence, specifically the particle swarm optimization (PSO) algorithm. We construct a complex search space of ARFs with two layers of cladding and nested tubes. There are three and four structures of cladding tubes in the first and second layer, respectively. The ARFs are optimized by using the PSO algorithm in terms of both the structures and the parameters. The optimal structure is obtained from a total of 415900 ARFs structures, with the lowest CL being 2.839×10-7 dB/m at a wavelength of 1.55 µm. We observe that the number of ARF structures with CL less than 1×10-6 dB/m in our search space is 370. These structures mainly comprise four designs of ARFs. The results show that the optimal ARF structures realized by the PSO algorithm are different from the ARFs reported in the previous literature. This means that the swarm intelligence accelerates the design and invention of ARFs and also provides new insights regarding the ARF structures. This work provides a fast and effective approach to design ARFs with special requirements. In addition to providing high-performance ARF structures, this work transforms the ARF designs from experience-driven to data-driven.

Large-mode-area all-solid anti-resonant fiber with single-mode operation for high-power fiber lasers.

We investigate the feasibility of applying an anti-resonant guiding mechanism in an all-solid anti-resonant fiber (AS-ARF) to achieve a large mode area (LMA) and single mode for high-power fiber laser applications. A novel, to the best of our knowledge, AS-ARF with nonuniform rods is proposed to enhance the single-mode property and enlarge the mode area. The numerical results show that the core diameter can expand to 57, 80, and 100 µm at the wavelengths of 1.064, 1.55, and 2 µm, respectively. The loss ratio of the lowest loss of higher-order modes to the loss of the fundamental mode can exceed 1000, 550, and 860 at the wavelength of 1.064, 1.55, and 2 µm; thus, robust single-mode operation can be ensured. Besides, the fiber can also be adapted to bent condition under certain heat load. These indicate that the proposed AS-ARF with nonuniform rods is a great candidate as an LMA fiber for high-power fiber lasers.

Use of machine learning to efficiently predict the confinement loss in anti-resonant hollow-core fiber.

The fundamental mode confinement loss (CL) of anti-resonant hollow-core fiber (ARF) is efficiently predicted by a classification task of machine learning. The structure-parameter vector is utilized to define the sample space of ARFs. The CL of labeled samples at 1550 nm is numerically calculated via the finite element method (FEM). The magnitude of CL is obtained by a classification task via a decision tree and k-nearest neighbors algorithms with the training and test sets generated by 290700 and 32300 labeled samples. The test accuracy, confusion matrices, and the receiver operating characteristic curves have shown that our proposed method is effective for predicting the magnitude of CL with a short computation runtime compared to FEM simulation. The feasibility of predicting other performance parameters by the extension of our method, as well as its ability to generalize outside the tested sample space, is also discussed. It is likely that the proposed sample definition and the use of a classification approach can be adopted for design application beyond efficient prediction of ARF CL and inspire artificial intelligence and data-driven-based research of photonic structures.

Design of a large-mode-area pixeled leakage channel fiber with an ultrahigh loss ratio for 2 µm operation.

A novel bend-resistant large mode area fiber with pixeled structure and leakage channel is proposed for application with a tight bend radius at the 2 µm wavelength region. Two layers of discontinuous fluorine-doped silica rods, which are called pixels, are introduced in the cladding. By removing four pixels, the loss of high-order modes is enhanced significantly due to the leakage channel. Bend properties of the fiber are investigated thoroughly by numerical simulations. As a result, a robust single-mode operation is obtained with an ultrahigh loss ratio (the minimum loss of high-order modes to the loss of fundamental mode) exceeding 3×105 when the fiber is bent with a tight radius as 10 cm. The effective mode area can reach 1764µm2. In addition, the fiber can keep high performance within a broad waveband from 1.75 to 2.15 µm. Large mode area and robust single-mode operation with a tight bend radius make this fiber beneficial to the development of 2 µm miniaturized fiber lasers.

Simultaneous Measurement of Curvature, Strain and Temperature Using a Twin-Core Photonic Crystal Fiber-Based Sensor.

A novel twin-core photonic crystal fiber-based sensor for simultaneous measurement of curvature, strain and temperature is proposed. The fiber sensor is constructed by splicing the homemade twin-core photonic crystal fiber between two segments of single mode fiber. Affected by the coupling between two cores, the transmission spectrum of the fiber sensor has different wavelength responses to curvature, strain, and temperature. The maximal sensitivities to curvature, strain and temperature are 10.89 nm/m-1, 1.24 pm/µÎµ and 73.9 pm/°C, respectively. Simultaneous measurement of curvature, strain and temperature can be achieved by monitoring the wavelength shifts of selected valleys in the transmission spectrum. Contrast experiment based on traditional twin-core fiber is carried out. Experimental results demonstrate that twin-core photonic crystal fiber-based sensor has higher sensitivity and better linearity than traditional twin-core fiber-based sensor.

Single-polarization single-mode double-ring hollow-core anti-resonant fiber.

A novel single-polarization single-mode double-ring hollow-core anti-resonant fiber with two single-polarization regions (1545-1553 nm and 1591-1596 nm) is proposed. Single-polarization guidance is achieved by coupling a polarized fundamental mode and silica mode by using different tube thicknesses. Specifically, when the wavelength is 1550 nm, only a single x-polarized fundamental mode with a low loss of 0.04 dB/m is propagated by a polarization extinction ratio of 17662 and minimum higher-order mode extinction ratio of 393 by optimizing the structural parameters. Furthermore, this fiber also exhibits high-performance bend resistance. The x-polarized FM loss is as low as 0.11 dB/m with single-polarization single-mode guidance when the proposed fiber was bent at a bend radius of 8 cm toward the x-direction.

Single-phase tunable white-light-emitting Sr3 La(PO4)3 : Eu2+, Mn2+ phosphor for white LEDs.

A novel, near-ultraviolet-excited white-light-emitting phosphor Sr3La(PO4)3:Eu2+, Mn2+ was synthesized by the solid-state method. Luminescence properties and the energy transfer mechanism were investigated in detail by photoluminescence spectra and decay curves. With the energy transfer between Eu2+ and Mn2+, a cold white light with chromaticity coordinates of (0.2790, 0.2273), correlated color temperature of 6501 K, Ra of 70, and external quantum efficiency of 35.5% was realized by changing the ratios of Eu2+ and Mn2+ in the Sr3La(PO4)3:Eu2+, Mn2+ phosphors. Resonant energy transfer from Eu2+ to Mn2+ ions has been demonstrated to be a dipole-dipole mechanism in Sr3La(PO4)3. The energy transfer efficiency increases with Mn2+ concentration increasing, and reaches a maximum of 55.6%.