Full C- and L-band covered second-order OAM mode generator based on a thinned helical long-period fiber grating.

A full C- and L-band covered second-order orbital-angular-momentum (OAM) mode generator has been proposed and experimentally demonstrated, which is realized by using a helical long-period fiber grating (HLPG) but inscribed in a thinned four-mode fiber. By optimizing the design of grating period and fiber diameter of the proposed HLPG, an ultra-broadband rejection filter with a depth of ∼23 dB, a bandwidth of ∼156 nm @-10 dB (ranging from 1522 nm to 1678 nm) and a bandwidth of ∼58 nm @-20 dB (ranging from 1574 nm to 1632 nm), has been successfully obtained as a typical sample. To the best of our knowledge, this is the first demonstration of such ultra-broadband second-order OAM mode generator by using only one fiber component, i.e., the thinned HLPG. In addition, the proposed generator is less polarization-dependent and less temperature-sensitive than those of the conventional HLPGs, which is believed to be considerably helpful to find potential applications of the device itself in wavelength division multiplexing (WDM) and OAM mode division multiplexing (MDM) optical fiber communication systems.

Temperature-insensitive high-sensitivity refractive index sensor based on a thinned helical fiber grating with an intermediate period.

A temperature-insensitive high-sensitivity refractive index sensor is proposed and experimentally demonstrated, which is based on utilization of a thinned helical fiber grating but with an intermediate period (THFGIP). Attributed to the reduced diameter and an intermediate period of the grating, the proposed sensor has a high surrounding refractive-index (SRI) sensitivity and a low temperature sensitivity. The average SRI sensitivity of the proposed sensor is up to 829.9 nm/RIU in the range of 1.3410-1.4480 RIU. Moreover, unlike the traditional sensitivity-enhancement method by increasing the waveguide dispersion factor, here the waveguide dispersion factor at the resonant wavelength was decreased by reducing the diameter of the fiber grating and as a result, the crosstalk effect due to the temperature change can be further suppressed. The proposed temperature-insensitive SRI sensor has the superiorities of simple structure, ease fabrication, and low cost, which could be found more potential applications in the SRI sensing fields.

Probe-type all-fiber tiny-displacement sensor based on orbital-angular-momentum interferometry.

In this study, a probe-type all-fiber tiny-displacement sensor is proposed and experimentally demonstrated, which is realized by using an all-fiber orbital-angular-momentum (OAM) interferometer, where a probe is especially adopted and inserted into the testing arm of the OAM interferometer. The proposed device takes full advantages of the OAM interferometer and the probe-type fiber sensor, making it completely available to the tiny-displacement measurement. As a result, changes in displacement (ranging from 0 nm to 750 nm) with a real resolution of ∼8.81 nm have been successfully measured. To our knowledge, this is the first demonstration of an all-fiber probe-type OAM interferometer, which may find potential application to high-precision tiny displacement in a small confined space.

Recognition of the orbital-angular-momentum spectrum for hybrid modes existing in a few-mode fiber via a deep learning method.

In this study, we theoretically and experimentally demonstrate that the convolutional neural network (CNN) in combination with the residual blocks and the regression methods can be used to precisely and quickly reconstruct the OAM spectrum of a hybrid OAM mode no matter how the consistent OAM modes have the same or different order indices in both the azimuthal and the radial direction. For cases of the simulation testing, the mean errors of all recognized parameters for hybrid OAM modes in a four-mode fiber (4MF) and a six-mode fiber (6MF) are smaller than 0.003 and 0.008, respectively. To the best of our knowledge, this is the first time that all the OAM modes, probably existing in the core of 4MFs or 6MFs, can be precisely and quickly recognized from intensity distribution of the hybrid OAM mode itself via the deep learning method.

Synthesizing the complex orbital-angular-momentum spectrum of hybrid modes existed in a few-mode fiber.

In this study, a simple and reliable method enabling to well synthesize the complex orbit-angular-momentum (OAM) spectrum of hybrid mode in a few-mode fiber is proposed and numerically demonstrated, which is realized by using the so-called inverse scattering method based on the genetic algorithm (GA), where the main Fourier components of a specially-selected ring in intensity distribution of the hybrid mode is used as the optimization objective. As a proof-of-concept example, power spectrum of a hybrid mode consisted of the first- and second-order OAM modes was successfully reconstructed with an accuracy higher than 0.99. This is the first time, to the best of our knowledge, that the complex OAM spectrum of a fiber hybrid mode consisted of more than two kinds of OAM modes is synthesized directly from the intensity distribution of the hybrid mode itself.

Operational Modal Analysis of Bridge Structures with Data from GNSS/Accelerometer Measurements.

Real-time dynamic displacement and acceleration responses of the main span section of the Tianjin Fumin Bridge in China under ambient excitation were tested using a Global Navigation Satellite System (GNSS) dynamic deformation monitoring system and an acceleration sensor vibration test system. Considering the close relationship between the GNSS multipath errors and measurement environment in combination with the noise reduction characteristics of different filtering algorithms, the researchers proposed an AFEC mixed filtering algorithm, which is an combination of autocorrelation function-based empirical mode decomposition (EMD) and Chebyshev mixed filtering to extract the real vibration displacement of the bridge structure after system error correction and filtering de-noising of signals collected by the GNSS. The proposed AFEC mixed filtering algorithm had high accuracy (1 mm) of real displacement at the elevation direction. Next, the traditional random decrement technique (used mainly for stationary random processes) was expanded to non-stationary random processes. Combining the expanded random decrement technique (RDT) and autoregressive moving average model (ARMA), the modal frequency of the bridge structural system was extracted using an expanded ARMA_RDT modal identification method, which was compared with the power spectrum analysis results of the acceleration signal and finite element analysis results. Identification results demonstrated that the proposed algorithm is applicable to analyze the dynamic displacement monitoring data of real bridge structures under ambient excitation and could identify the first five orders of the inherent frequencies of the structural system accurately. The identification error of the inherent frequency was smaller than 6%, indicating the high identification accuracy of the proposed algorithm. Furthermore, the GNSS dynamic deformation monitoring method can be used to monitor dynamic displacement and identify the modal parameters of bridge structures. The GNSS can monitor the working state of bridges effectively and accurately. Research results can provide references to evaluate the bearing capacity, safety performance, and durability of bridge structures during operation.

Metabolomic investigation into variation of endogenous metabolites in professional athletes subject to strength-endurance training.

Strength-endurance type of sport can lead to modification of human beings' physiological status. The present study aimed to investigate the alteration of metabolic phenotype or biochemical compositions in professional athletes induced by long-term training by means of a novel systematic tool, metabolomics. Resting venous blood samples of junior and senior male rowers were obtained before and after 1-wk and 2-wk training. Venous blood from healthy male volunteers as control was also sampled at rest. Endogenous metabolites in serum were profiled by GC/TOF-MS and multivariate statistical technique, i.e., principal component analysis (PCA), and partial least squares projection to latent structures and discriminant analysis (PLS-DA) were used to process the data. Significant metabolomic difference was observed between the professional athletes and control subjects. Long-term strength and endurance training induced distinct separation between athletes of different exercise seniority, and training stage-related trajectory of the two groups of athletes was clearly shown along with training time. However, most of these variations were not observed by common biochemical parameters, such as hemoglobin, testosterone, and creatine kinase. The identified metabolites contributing to the classification included alanine, lactate, beta-d-methylglucopyranoside, pyroglutamic acid, cysteine, glutamic acid, citric acid, free fatty acids, valine, glutamine, phenylalanine, tyrosine, and so on, which were involved in glucose metabolism, oxidative stress, energy metabolism, lipid metabolism, amino acid metabolism. These findings suggest that metabolomics is a promising and potential tool to profile serum of professional athletes, make a deep insight into physiological states, and clarify the disorders induced by strength-endurance physical exercise.