Search Results (226)

The coupled characterization of overburden movement, surrounding-rock failure evolution, and mine-pressure response remains difficult during high-intensity longwall mining because these processes are commonly measured and interpreted using separate data streams. In this study, a multi-source sensing and interpretation framework was established for the S8310 longwall face of Yangmei No. 1 Mine by integrating physical similarity simulation, underground monitoring, UDEC numerical modeling, region-of-interest (ROI) image-feature extraction, and sliding-window long short-term memory (LSTM) analysis. Fiber Bragg grating (FBG) sensors and conventional monitoring were used to obtain key-stratum deformation and support-pressure responses. A joint-state-based damage index (DI) was constructed from fixed-ROI UDEC outputs to quantify progressive structural activation beneath the key stratum. The results indicate that the overburden evolved from global bending and local crack initiation to fracture expansion, interface degradation, and interlayer slip. In the physical model, the initial weighting interval was approximately 37.5 cm, and the periodic weighting intervals were mainly 13.3–15.7 cm; these correspond to prototype-scale distances of approximately 37.5 m and 13.3–15.7 m, respectively. Peak abutment pressure occurred approximately 9–17 cm ahead of the face at the model scale, with a peak coefficient of 1.40–1.68. When the prototype advance distance increased from 37.5 m to 80.3 m, the ROI exhibited enhanced joint activation and a transition from local slip to continuous tensile opening and slip. For the pressure-sequence analysis, the 24-step-window LSTM produced lower errors than the 36- and 48-step-window LSTM models under the same preprocessing and training settings. Additional persistence, moving-average, and random forest baselines were added to clarify the prediction context: the persistence baseline performed strongly because of the high short-term autocorrelation of the pressure series, whereas the 24-step-window LSTM outperformed the moving-average and random forest baselines. Accordingly, the LSTM module is interpreted as an auxiliary temporal-pattern identification tool rather than as an exclusive optimal predictor. The comparison between DI evolution and pressure-prediction behavior suggests that rapid DI growth is mechanically consistent with stronger pressure-sequence non-stationarity and increased prediction deviation near active mine-pressure events. The proposed framework provides a sensor-oriented and physically interpretable approach for linking overburden failure evolution with mine-pressure response in longwall mining. Full article

This work introduces a refractometric sensor based on multiple cascaded Michelson interferometers fabricated with long-period fiber gratings to classify different commercial brands of paracetamol as well as to measure solutions with different paracetamol concentrations. The operating principle is based on the interaction of light with the fiber–medium interface, such that variations in the refractive index provoke changes in the spectrum profile that can be measured at the output of the device. The brands used were Medley, Mejoral 500, Tempra, Tylenol, and excipient-free paracetamol (EFP). A support vector machine (SVM) classifier was used to recognize each brand with an F1-score of 92%. Also, using a projection to latent structures regression (PLSR), it was possible to quantify the paracetamol concentration with the best limit of detection (LOD) of 1.58 g/kg for the Mejoral brand. Full article

In millimeter-wave frequency-modulated continuous-wave (FMCW) multiple-input multiple-output synthetic-aperture radar (MIMO-SAR) imaging, platform displacement beyond the spatial Nyquist limit during a slow-time sampling interval creates aperture gaps, causing azimuth aliasing and degraded resolution. This paper proposes a spectral-prior-guided Swin TransUnet (SSTU) method for suppressing azimuth ambiguity in sparse moving-array imaging. Gaussian soft labels derived from point-scatterer positions formulate localization as heatmap regression and guide mainlobe learning. A two-dimensional fast Fourier transform (2D-FFT) layer then constructs a range–azimuth spectrum that exposes main peaks, sidelobes, and periodic grating lobes. A convolutional encoder extracts local spectral features, Swin Transformer blocks model long-range ambiguity correlations, and a U-Net-style multiscale decoder reconstructs high-resolution range–azimuth images. Simulations show that SSTU reliably recovers multiple point targets from noise and grating lobes despite substantial aperture gaps. At 60% aperture sparsity and signal-to-noise ratio (SNR) above −6 dB, it achieves a root mean square error (RMSE) below ${10}^{-2}$ and an azimuth ambiguity suppression ratio better than −30 dB, outperforming conventional methods. Measurements using a 77 GHz radar platform further demonstrate high-quality outdoor imaging of randomly distributed strong scatterers at 60% moving-aperture sparsity. Full article

To circumvent the prohibitive cost of large-format infrared focal plane arrays and the significant spatial–spectral mismatch caused by spectral smile in conventional long-slit configurations, this work develops a low-cost short-wave infrared (SWIR, 1000–2500 nm) hyperspectral imaging system utilizing digital micromirror device (DMD) scanning paired with a single-element detector. A comprehensive analytical model for a prism–reflection grating (P-RG) compound dispersive element is established, enabling the joint optimization of the prism apex angle and grating period to achieve quantitative compensation of spectral distortion across the entire waveband. Based on this model, the optical system is integrated and optimized, while a centroid localization algorithm is implemented to facilitate online calibration of model parameters and real-time reconstruction of the hyperspectral data cube at the DMD plane. Experimental results demonstrate that both smile and keystone distortions are suppressed below $5\mathsf{\mu }\mathrm{m}$ throughout the 1000–2500 nm range, which is superior to the single DMD pixel pitch of $7.6\mathsf{\mu }\mathrm{m}$. The full-field modulation transfer function (MTF) at the Nyquist frequency (32.9 lp/mm) exceeds 0.7, approaching the diffraction limit. Characterization confirms that the system provides 510 spectral channels with an average resolution of 3.57 nm and a spatial resolution of 2.5 $\mathsf{\mu }\mathrm{m}$. By effectively eliminating spectral overlap and cross-column crosstalk on the DMD encoding surface, this system provides a high-fidelity optical front-end for single-pixel imaging, offering a viable technical pathway for the development of affordable SWIR hyperspectral instrumentation. Full article

Nowadays, optical fiber-based biosensors are widely used in various fields, particularly in medical diagnostics and the selection of appropriate treatments for certain diseases. One example is cerebral ischemic disease, where many biomarkers are released and provide valuable information about the severity and progression of the disease. In this study, a long-period fiber grating (LPFG) biosensor was developed using a standard single-mode optical fiber and monoclonal antibody (IL-6 mAb) as the biological recognition element to detect IL-6, which is a protein associated with the inflammatory process. The assembly of the LPFG biosensor was characterized through optical and electronic microscopy to observe morphological changes at different stages of fabrication and the detection process. Additionally, micro-infrared spectroscopy was employed to identify functional groups in the protein region linked to the presence of IL-6. Experimental data were analyzed using principal component analysis, confirming the biosensor’s ability to detect IL-6 and providing insights into its fabrication process. Full article

Wearable data gloves often suffer from electromagnetic interference, insufficient substrate stability, and limited capability for multi-degree-of-freedom motion measurement. To address these limitations, a flexible glove incorporating a hybrid POF-FBG sensing scheme was designed and fabricated. Plastic optical fibers (POFs) were side-polished and patterned with long-period gratings to improve sensitivity to wrist flexion-extension and abduction-adduction. Then fiber Bragg gratings (FBGs) were embedded in a polydimethylsiloxane substrate and encapsulated using thermoplastic polyurethane fixtures to reduce the influence of skin stretching and improve measurement accuracy of finger-joint angle. Moreover, a thermoplastic polyurethane skeleton with an adaptive sliding-rail structure was 3D printed to maintain the stability of the sensor placement at the joints. Experimental results demonstrated the mean absolute errors of 4.06°, 1.38° and 1.70° for wrist flexion-extension, abduction-adduction and finger-joint bending, respectively, along with excellent gesture classification using a support vector machine algorithm, which indicates great potential in virtual reality interaction and hand rehabilitation applications. Full article

Typically, response analysis of optical fiber biosensors focuses on changes in amplitude and wavelength shifts in the biosensor spectrum; therefore, not all of the spectral range is used for this analysis. On the other hand, if the entire spectrum is used, it is possible to leverage the current data in the spectrum and thus improve the performance of the biosensor. To do this, it is necessary to analyze a large amount of data present in each measured spectrum. This task can be made easier by using dimensionality reduction techniques. In addition, it is necessary to establish which spectral regions provide relevant information. Scaling techniques are mathematical data preprocessing tools used in machine learning to adjust the numerical scale of variables so that they have comparable weight and even highlight those characteristics that provide more information. To our knowledge, the use of these techniques in the development of optical fiber biosensors is not very common, which is why we believe they represent an attractive topic of study in this area. With the help of scaling techniques, we can modify the scale of the data so that all the information contained in the spectrum is used, regardless of its magnitude. In this work, two biosensors based on a chirped long period fiber grating (CLPFG) and a chirped Mach–Zehnder interferometer (CMZI) were developed for the detection of interleukin-10 (IL-10). Principal component analysis (PCA) was used as a dimensionality reduction technique together with a support vector machine (SVM) classifier with four different scaling techniques, standardization, minimum–maximum scaling, robust scaling, and a custom transformer, to compare the IL-10 detection performance of the biosensors. The results showed that robust scaling in CMZI performed best in detecting IL-10, with an F1-score equal to 1, as well as better reliability in detecting the protein. Full article

The rapidly increasing demand for compact, high-performance beam-steering solutions in LiDAR systems has driven substantial advances in vertical-cavity surface-emitting laser (VCSEL) technologies. In this paper, we present a high-power, ultra-low-divergence VCSEL-based beam scanner array that integrates multi-wavelength seed lasers with extended-length optical amplifiers, thereby simultaneously achieving wide-angle beam steering, near-diffraction-limited beam quality, and watt-class output power. The proposed architecture exploits slow-light modes supported by laterally extended VCSEL waveguides incorporating precisely engineered surface gratings. This design enables fully electronic beam steering over an angular range exceeding 30°, with an angular resolution surpassing 1600 resolvable points. Systematic characterization of seed lasers with distinct grating periods confirms robust single-mode operation and yields a cumulative wavelength tuning range exceeding 22 nm. When integrated with optical amplifiers up to 6 mm in length, the system achieves a record-low beam divergence of 0.018°, approaching the theoretical diffraction limit. Under continuous-wave operation and without active thermal management, the device delivers output powers exceeding 1.6 W. By overcoming the long-standing trade-offs among steering range, beam quality, and output power, this work establishes a transformative paradigm for compact VCSEL-based beam-steering systems and represents a significant step toward next-generation solid-state LiDAR technologies. Full article

This paper presents a systematic review conducted under the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework, analyzing 286 scientific articles focused on vibration-based predictive maintenance strategies for wind turbines within the context of advanced Prognostics and Health Management (PHM). The review combines international standards (ISO 10816, ISO 13373, and IEC 61400) with recent developments in sensing technologies, including piezoelectric accelerometers, microelectromechanical systems (MEMS), and fiber Bragg grating (FBG) sensors. Classical signal processing techniques, such as the Fast Fourier Transform (FFT) and wavelet-based methods, are identified as key preprocessing tools for feature extraction prior to the application of machine-learning-based diagnostic algorithms. Special emphasis is placed on machine learning and deep learning techniques, including Support Vector Machines (SVM), Random Forest (RF), Convolutional Neural Networks (CNN), Long Short-Term Memory networks (LSTM), and autoencoders, as well as on hybrid digital twin architectures that enable accurate Remaining Useful Life (RUL) estimation and support autonomous decision-making processes. The bibliometric and case study analysis covering the period 2020–2025 reveals a strong shift toward multisource data fusion—integrating vibration, acoustic, temperature, and Supervisory Control and Data Acquisition (SCADA) data—and the adoption of cloud-based platforms for real-time monitoring, particularly in offshore wind farms where physical accessibility is constrained. The results indicate that vibration-based predictive maintenance strategies can reduce operation and maintenance costs by more than 20%, extend component service life by up to threefold, and achieve turbine availability levels between 95% and 98%. These outcomes confirm that vibration-driven PHM frameworks represent a fundamental pillar for the development of smart, sustainable, and resilient next-generation wind energy systems. Full article

Metal halide perovskites have emerged as promising photoactive materials for highly efficient photodetectors; however, the inherent instability of perovskite materials in oxygen and moisture limits their practical applications. In this study, the highly moisture-sensitive characteristics of the quasi-2D perovskite nanocrystals were used to fabricate a long-period grating (LPG) humidity sensor based on the perovskite/polyacrylonitrile (PAN) hybrid nanofibers film. The pure-bromide quasi-2D perovskite nanocrystals were in situ synthesized and encapsulated in the PAN matrix on the fiber grating via an electrospinning technique. Humidity-induced variation in the complex permittivity of perovskites can alter the evanescent field of the co-propagating cladding modes, resulting in changes in both resonant amplitude and wavelength in the transmission spectrum of the LPG. These effects yielded an intensity sensitivity of ~0.21 dB/%RH and a wavelength sensitivity of ~18.2 pm/%RH, respectively, in the relative humidity range of 50–80%RH. The proposed LPG sensor demonstrated a good performance, indicating its potential application in the humidity-sensing field. Full article

Hydrogen, a promising clean energy carrier, needs safe detection due to its flammability. Conventional electrical hydrogen sensors have drawbacks like high operating temperatures, poor selectivity and ignition risks. We propose an optical sensor using long-period fiber gratings (LPGs) coated with Pt-SiO₂ nanomaterials. It works via catalytic reaction: H₂ reacts with O₂ on Pt nanoparticles, releasing heat that shifts LPG’s resonant wavelength. Structural characterization showed porous SiO₂ with uniform Pt, ensuring efficiency and stability. Experiments proved it sensitively responds to 0.5–2.5% H₂ (max wavelength shift 7.544 nm), with fast response/recovery, good repeatability/reversibility. Logistic fitting (R² = 0.999) confirmed strong correlation. This sensor, safe, sensitive and stable, has great potential for real-time H₂ monitoring in critical environments. Full article

This paper proposes a grating Moiré fringe signal compensation method based on Variational Mode Decomposition (VMD) to address signal errors in grating encoders. VMD decomposes Moiré fringe signals into multiple amplitude-modulated and frequency-modulated components, and realizes noise compensation through parameter optimization and signal reconstruction. The Multi-Strategy Particle Swarm Optimization (MSPSO) enhances optimization performance via adaptive inertia weight adjustment and chaotic perturbation, solving the problems of mode mixing or over-decomposition caused by blind parameter selection in traditional VMD. A hardware-software co-design test system based on ZYNQ FPGA is developed, which optimally allocates tasks between the Processing System and Programmable Logic, resolving issues of large data volume and long computation time in traditional systems. The compensation scheme provides excellent signal processing performance. The experimental tests on random periodic signals, triangular waves and square waves with different duty cycles have demonstrated the robustness of this scheme. After compensation, the output signal exhibits excellent sinuosity and orthogonality, with harmonic components and noise in the frequency domain almost negligible. It provides a practical solution for high-precision measurement in ultra-precision machining, semiconductor manufacturing, and automated control. Full article

Utilization of natural processes can reduce the complexity and production cost of any device by limiting the necessary steps in the production scheme, especially when it comes to fibers with periodic changes in refractive index. One such process is the nematic–isotropic phase separation of liquid crystal-based composite confined in 1D space. In this paper, we analyze the behavior of polymer-stabilized liquid crystal-based self-assembled periodic structures in an external electric field. We performed a detailed analysis regarding the reorientation of liquid crystal molecules under two orthogonal directions of the external electric field applied to the examined sample. It was demonstrated that the period of the polymerized structure remains constant until full reorientation, as the electric field induces the formation of new periodic defects in LC orientation. Consequently, the structure’s effective birefringence changes quite drastically, and this observed change depends on the direction of the electric field vector. The obtained results seem promising when it comes to application of the proposed periodic structures as voltage or electric field sensors operating as long-period fiber gratings or fiber Bragg gratings for the visible or near-infrared spectral regions. Full article

This paper proposes a long-period grating (LPG) based on double-clad fibers (DCFs) for multi-parameter sensing. The sensor consists of cascaded-input single-mode fibers (SMF), DCF, and output SMF. Multi-parameter detection is realized by utilizing the different sensing characteristics of the resonance peak under different physical parameters. The experiment results show that within the temperature range of 30–100 °C, the maximum sensitivity is 66.37 pm/°C. For the refractive index (RI) measurement, the tested range is 1.3309–1.3888 and the maximum sensitivity is −45.84 nm/RIU. Regarding curvature detection, the tested range is 0.6928–1.6971 m⁻¹ and the maximum sensitivity is −2.022 nm/m⁻¹. In addition, the sensor has a symmetrical structure, so its measurement is not restricted by the incident direction of light, thus having flexibility in practical use. This research not only contributes to the advancement of optical fiber sensor technology but also has significant implications for practical applications in industry, the environment, and healthcare. Full article