Search Results (1,358)

To address traditional beer detection limitations, this study proposes a rapid NIRS-based method for detecting key indicators and verifying authenticity. Designing Single-task (STL) and Multi-task learning (MTL) strategies, it employs Variable Importance in Projection for wavelength selection. Deep spectral features were extracted utilizing a Multi-Head Attention (MHA)-fused Convolutional Neural Network (CNN-MHA), Long Short-Term Memory (LSTM-MHA), and hybrid CNN-LSTM-MHA networks. To further enhance model performance, the Bayesian Optimization Algorithm globally optimized network hyperparameters in STL, alongside hyperparameters and multi-task loss weights in MTL. Partial least squares regression, support vector machine regression, and partial least squares discriminant analysis models were established using these features. Results indicate that the MTL-based CNN-LSTM-MHA network effectively learns shared features across multiple tasks, significantly improving model generalization. Specifically, the coefficients of determination ($R^2$) for alcohol content and original wort concentration in the validation set were 0.996 and 0.997, respectively, with relative root mean square errors (rRMSE) of 2.024% and 2.515%. In the independent test set, the $R^2$ were 0.995 and 0.991, with rRMSE of 2.515% and 2.087%, respectively. Furthermore, 100% classification accuracy was achieved across all datasets. This method provides an efficient technical solution for beer market regulation and real-time detection in production processes. Full article

The growing demand for high-fidelity, multi-parameter, distributed sensing in critical domains such as structural health monitoring, oil and gas exploration, and secure perimeter surveillance is pushing traditional optical fiber sensors (OFS) to their performance limits. Although conventional multiplexing techniques such as time-division and wavelength-division multiplexing (TDM, WDM) have been commercially successful, they are rapidly approaching fundamental bottlenecks in sensor density, spatial resolution, and data capacity. This review argues that the synergistic convergence of space-division multiplexing (SDM) and artificial intelligence (AI) represents a paradigm shift, enabling a new generation of intelligent, high-dimensional sensing networks. We comprehensively survey the state of the art in SDM-based OFS, detailing the operating principles and applications of multi-core fibers (MCFs) for ultra-dense sensor arrays and 3D shape sensing, as well as few-mode fibers (FMFs) for mode-division multiplexing and enhanced multi-parameter discrimination. However, the unprecedented spatial parallelism provided by SDM introduces significant challenges, including inter-channel crosstalk, complex signal demultiplexing, and massive data volumes. This paper systematically explores how AI, particularly machine learning (ML) and deep learning (DL), is being leveraged not merely as a tool but as an indispensable core technology to mitigate these impairments. We critically analyze AI’s role in digital crosstalk suppression, intelligent mode demultiplexing, signal denoising, and solving complex inverse problems for parameter estimation. Furthermore, we highlight how this AI–SDM synergy enables capabilities beyond the reach of either technology alone, such as super-resolution sensing and predictive analytics. The discussion is extended to include the critical supporting pillars of this ecosystem, such as advanced interrogation techniques and the associated data management challenges. Finally, we provide a forward-looking perspective on the trajectory of the field, outlining a path toward cognitive sensing networks that are self-calibrating, adaptive, and capable of autonomous decision-making. This review is intended to serve as a foundational reference for researchers and engineers at the intersection of photonics and intelligent systems, illuminating the pathway toward tomorrow’s intelligent sensing infrastructure. Full article

We present an innovative, cost-effective framework integrating laboratory Hyperspectral Imaging (HSI) of the Bechar 010 Lunar meteorite with ground-based lunar HSI and supervised Machine Learning (ML) to generate high-fidelity mineralogical maps. A 3 mm thin section of Bechar 010 was imaged under a microscope with a 30 mm focal length lens at 150 mm working distance, using 6x binning to increase the signal-to-noise ratio, producing a data cube (X × Y × $\lambda$ = $791\times 1024\times 224$, $0.24$ mm × $0.2$ mm resolution) across 400 nm to 1000 nm (224 bands, $2.7$ nm spectral sampling, $5.5$ nm full width at half maximum spectral resolution) using a Specim FX10 camera. Ground-based lunar HSI was captured with a Celestron 8SE telescope (3 km/pixel), yielded a data cube ($371\times 1024\times 224$). Solar calibration was performed using a Spectralon reference (99% reflectance < 2% error) ensured accurate reflectance spectra. A Support Vector Machine (SVM) with a radial basis function kernel, trained on expert-labeled spectra, achieved 93.7% classification accuracy (5-fold cross-validation) for olivine (92% precision, 90% recall) and pyroxene (88% precision, 86% recall) in Bechar 010. LIME analysis identified key wavelengths (e.g., 485 nm, 22.4% for M3; 715 nm, 20.6% for M6) across 10 pre-selected regions (M1 to M10), indicating olivine-rich (Highland-like) and pyroxene-rich (Mare-like) compositions. SAM analysis revealed angles from $0.26$ rad to $0.66$ rad, linking M3 and M9 to Highlands and M6 and M10 to Mares. K-means clustering of Lunar data identified 10 mineralogical clusters (88% accuracy), validated against Chandrayaan-1 Moon mineralogy Mapper (${\mathrm{M}}^3$) data (140 m/pixel, 10 nm spectral resolution). A novel push-broom HSI approach with a telescope achieves 0.8 arcsec resolution for lunar spectroscopy, inspiring full-sky multi-object spectral mapping. Full article

Environmental monitoring systems require robust uncertainty quantification for effective decision-making in complex ecological processes. Harmful algal blooms represent a critical challenge where prediction uncertainty directly impacts resource allocation and response timing, yet current remote sensing-based prediction systems provide only deterministic classifications without confidence measures. This gap between algorithmic predictions and actionable risk assessment limits operational utility for stakeholders managing water quality under varying risk tolerances. This study developed a transferable probabilistic forecasting framework integrating Sentinel-2 multispectral imagery with quantile regression and ensemble machine learning to generate continuous confidence indicators for cyanobacteria density prediction, demonstrated through its application to Lake Okeechobee, Florida. The methodology combines spectral indices extracted from Sentinel-2 data with XGBoost for quantile regression at 0.05, 0.50, and 0.95 probability levels, and LightGBM for multi-horizon temporal forecasting. Sentinel-2’s 13 spectral bands spanning visible to shortwave infrared wavelengths, combined with its 5-day revisit frequency provide a spectrally rich and temporally dense input space that is well-suited to gradient boosting methods such as XGBoost, which can exploit complex nonlinear interactions among spectral features to distinguish cyanobacterial signatures from background water constituents. LightGBM achieved mean absolute percentage errors of 2.9% for 10-day forecasts and 5.7% for 20-day forecasts, outperforming conventional regression models. The framework generates 90% prediction intervals that enable reliable risk classifications for operational bloom management. This approach bridges the gap between satellite-based algal bloom detection and actionable decision-making by quantifying predictive uncertainty, representing a shift from binary classifications to probability-based environmental monitoring systems that accommodate varying stakeholder risk tolerances in water quality management applications. Full article

Hafnium oxide (HfO₂) is predominantly used as a high-index material in multi-layer dielectric coatings for high-peak- and high-average-power lasers, but laser damage often initiates within the HfO₂ layers despite their wide bandgap. Oxygen deficiency during deposition can introduce vacancy-related sub-bandgap states and absorptive defects, lowering damage resistance. This study investigates how oxygen flow during HfO₂ deposition with ion beam sputtering (IBS) affects its stoichiometry, defect formation, and nanosecond laser-induced damage threshold (LIDT) and whether single-layer trends predict multilayer performance. Single layers were deposited at varying oxygen flows, characterized for optical and structural properties, and tested for the LIDT at 1064 nm and 355 nm. Increasing oxygen flow drove the layer toward near-stoichiometric HfO₂, reduced the refractive index, and altered the density of surface pinhole-like features. The single-layer LIDT at 355 nm increased with oxygen, whereas the 1064 nm LIDT was comparatively less sensitive to oxygen flow, consistent with the wavelength-dependent roles of absorptive precursors and microstructural defects. In contrast, a HfO₂-based high-reflector (HR) showed a higher LIDT at lower oxygen flow, indicating that the family of damage precursors changes between single layers and multilayers; in stacks, structural properties such as stress, gas entrapment and thermal dissipation may outweigh the isolated absorptive defects found in single layers. These results demonstrate that the optimal oxygen flow condition depends on both LIDT wavelength and film architecture. We identified, for single layers, a 15–35 sccm window for maximizing the 1064 nm LIDT and a high-flow optimum (45 sccm) for the 355 nm LIDT and, for 355 nm HR stacks, a distinct lower-flow regime (~10 sccm). Full article

Underwater imaging is generally subject to complex degradation issues such as color distortion, contrast degradation, and detail blurring due to the selective absorption and scattering of light wavelengths by water. Existing deep learning methods have limitations in the collaborative optimization of local details and global color. To address this issue, this paper proposes a multi-scale enhancement network based on global and local feature fusion. By integrating the advantages of CNN and Transformer, it achieves joint optimization of global color correction and local detail enhancement. Specifically, MGLFNet extracts global and local features of the image through the global and local feature fusion block in the core component of the multi-scale convolution–Transformer block and performs dynamic fusion. Meanwhile, to extract features at different scales to enhance performance, we design a multi-scale convolution feed-forward network. Through the action of the fusion module and the feed-forward network, a color-rich and detail-clear enhanced image is obtained. A large number of experimental results show that MGLF-Net outperforms comparison methods in both qualitative and quantitative evaluations of visual quality, with PSNR and SSIM values of 25.37 and 0.918 on the UIEB dataset, respectively, as well as low memory usage and computational resource requirements. In addition, detailed ablation experiments prove the effectiveness of the core components of the model. Full article

The Nd:GYSAG crystal enables multi-wavelength near-infrared laser output, with adjustable wavelengths tailored for specific application requirements, making it highly valuable for space-borne water vapor detection. This study reports, for the first time, the side-pumping characteristics and electro-optical Q-switching performance of this crystal. Using Ø3 × 73 mm and Ø4 × 73 mm crystal rods doped with 1.21 at.% Nd:GYSAG (chemical formula Nd_0.033Gd_0.93Y_1.79Sc_0.70Al_4.54O_11.99), 1060.4 nm laser output was achieved under 808 nm laser diode (LD) side-pumping at a repetition rate of 100 Hz and a pump pulse width of 250 μs. The experimental results show that the Ø4 × 73 mm rod had a higher laser threshold but exhibited significantly superior slope efficiency and maximum output power compared to the Ø3 × 73 mm rod. Using a flat–flat resonator, optimal laser performance was obtained with an output coupler transmission of 35%, yielding a slope efficiency of 37.2%. A maximum output energy of 179.4 mJ was achieved at a pump energy of 646 mJ. Thermal lensing effects were compensated using a flat–convex cavity, leading to improved laser performance and beam quality. Electro-optical Q-switching experiments were conducted using a KD*P crystal. A comparison between voltage-applied and voltage-removed Q-switching techniques revealed superior performance for the voltage-applied method. High-performance laser output was realized, achieving a maximum pulse energy of 59.6 mJ, a pulse width of 14.93 ns, and a peak power of 3.99 MW. This study provides an important foundation for the development of near-infrared laser devices based on Nd:GYSAG. Full article

Wavelength modulation spectroscopy (WMS) is a representative implementation of tunable diode laser absorption spectroscopy (TDLAS), enabling reliable gas component analysis with concentration-related information derived from harmonic component extraction, while offering enhanced noise immunity for trace gas sensing in open environments. However, due to the strong coupling between laser wavelength and intensity, wavelength modulation inevitably introduces residual amplitude modulation (RAM), which significantly degrades measurement accuracy. To address this issue, this study introduces a RAM suppression algorithm based on multiple harmonic component decoupling (MHCD), using the second-harmonic lateral peak inclination angle (LPIA) as a characteristic indicator. Unit harmonic operators for the first, second, and third harmonics are designed, and an original harmonic reconstruction model is established via linear superposition of harmonic components. The optimal harmonic component ratio is determined at the composite operator with the maximum cross-correlation coefficient, and RAM noise is eliminated through a multi-harmonic decoupling matrix. Repetitive measurements on 22 mm pharmaceutical vials with 4% oxygen concentration demonstrate that MHCD reduces the second-harmonic LPIA from 18.07° to 8.56°. Concentration discrimination experiments conducted on seven groups of 22 mm vials with 2% concentration steps (0–12%) show that MHCD increases the true positive rate by 6–11% and decreases the false positive rate by 4–9%, confirming its effectiveness for pharmaceutical online inspection applications. Full article

The constant growth of IP data traffic, driven by sustained annual increases surpassing 26%, is pushing current optical transport infrastructures towards their capacity limits. Since the deployment of new fiber cables is economically demanding, ultra-wideband transmission is emerging as a promising cost-effective solution, enabled by multi-band amplifiers and transceivers spanning the entire low-loss window of standard single-mode fibers. In this scenario, an accurate modeling of the frequency-dependent fiber parameters is essential to reliably model optical signal propagation. In particular, the combined impact of attenuation variations with frequency and inter-channel stimulated Raman scattering (SRS) fundamentally shapes the power evolution of wide wavelength division multiplexing (WDM) combs and directly affects nonlinear interference (NLI) generation, as well as the amount of ASE noise. In this work, we review a set of analytical approximations, based on phenomenological approaches, for frequency-dependent attenuation and Raman scattering gain, and analyze their impact on achieving an effective balance between computational efficiency and physical fidelity. Through extensive analyses performed with the open-source software GNPy (version 2.12, Telecom Infra Project) on an optical line system exploring multi-band scenarios spanning C+L+S, C+L+E, and U-to-E transmission, we demonstrate that the proposed approximations reproduce the reference SRS power evolution and NLI profiles with root mean square errors (RMSEs) consistently below 0.03 dB, and down to the 10⁻³–10⁻² dB range for the most accurate configurations. Although the current implementation does not yet provide a direct reduction in computational time, the proposed framework lays the groundwork for future developments toward closed-form or semi-analytical solutions, enabling more efficient modeling and optimization of ultra-wideband optical transmission. Full article

Most of the existing carbon dot (CD)-based afterglow materials are limited to a single emission mode of either room-temperature phosphorescence (RTP) or delayed fluorescence (DF), which makes it difficult to meet the application requirements of advanced anti-counterfeiting and multi-level information encryption. Therefore, the development of CD-based composite materials with multi-mode afterglow emission, long lifetime and high stability holds significant research significance and application value. In this study, long-afterglow manganese/nitrogen co-doped CDs@boric acid (BA) composites (Mn, N-CDs @BA) are successfully prepared, and their optical properties and emission mechanism are clarified. The results demonstrate that the Mn, N-CDs @BA composites exhibit wavelength-dependent dual-afterglow emission characteristics of RTP and DF. Under 254 nm ultraviolet (UV) light excitation, they exhibit DF emission with an average lifetime of 903.36 ms. Under 365 nm UV light excitation, RTP emission with an average lifetime of 354.43 ms is observed. Moreover, the afterglow color exhibits time dependence. Based on the triple emission modes (fluorescence, RTP and DF) of the Mn, N-CDs @BA composites, optical patterns were designed and fabricated, and counterfeit-resistant and unclonable anti-counterfeiting and high concealment information encryption were successfully achieved. This work develops a potentially feasible approach for next-generation advanced optical anti-counterfeiting and information encryption systems. Full article

Beyond supporting ultra-high-capacity data transmission, metropolitan and access networks are expected to enable real-time infrastructure monitoring, driving the emergence of integrated sensing and communication (ISAC). Distributed acoustic sensing (DAS) has proven to be well-suited to urban sensing application requirements, yet its seamless integration into ISAC remains challenging—conventional high-peak-power sensing pulses in DAS induce nonlinear crosstalk in communication channels. DAS inherently suffers from interference fading due to single-frequency laser sources, which limits sensitivity. Here, we propose an ISAC architecture based on an electro-optic (EO) comb and a 7-core fiber, achieving nonlinearity-suppressed self-homodyne transmission and fading-suppressed DAS. Unmodulated comb lines and sensing pulses are polarization-multiplexed into orthogonal polarization states within the central core to minimize nonlinear crosstalk while delivering local oscillators (LOs) for wavelength division multiplexing (WDM) coherent transmission within six outer cores—achieving 10.56 Tbit/s capacity. In addition to supporting WDM transmission, the EO comb’s wavelength diversity is also exploited to enhance DAS performance. Specifically, a dual-pulse probe loaded onto four comb lines yields a 6 dB signal-to-noise ratio gain and a 64% reduction in fading occurrences, achieving a sensitivity of 1.72 $p\epsilon /\sqrt{\mathrm{Hz}}$ with 8 m spatial resolution. Moreover, our system supports simultaneous multi-wavelength backscatter detection in sensing and simplified digital signal processing in self-homodyne communication, reducing receiver complexity and cost. Our work presents a scalable, energy-efficient ISAC framework that unifies high-capacity communication with high-sensitivity sensing, providing a blueprint for future intelligent optical networks. Full article

Clouds are a key factor affecting solar radiation, and their dynamic variations directly cause uncertainty and fluctuations in photovoltaic (PV) power output. To improve PV power prediction accuracy, this paper proposes an enhanced short-term photovoltaic power forecasting approach based on a hybrid neural network architecture using features extracted from satellite cloud images. First, a dual-layer image fusion method is developed for satellite cloud images from different wavelengths and spectral bands, effectively improving fusion accuracy. Second, texture descriptors derived from the Gray-Level Co-occurrence Matrix and multiscale information obtained via the wavelet transform are employed for feature extraction from fused images. Combined with a residual network (ResNet), an optical flow method, as well as an LSTM-based temporal modeling module, multidimensional features of the predicted cloud images are obtained. An improved Bayesian optimization (IBO) algorithm is then employed to derive the optimal fused features, thereby improving the matching between cloud image features and PV power. Third, an enhanced hybrid architecture integrating a convolutional neural network and long short-term memory units with a multi-head self-attention mechanism is developed. Numerical weather prediction (NWP) meteorological features are incorporated, and a tilted irradiance model is introduced to calculate the solar irradiance received by PV modules for use in near-term photovoltaic power forecasting. Finally, measurements collected at a photovoltaic power plant located in Hebei Province are used to validate the proposed method. The results show that, relative to the SA-CNN-MSA-LSTM and BO-CNN-LSTM models, the developed approach lowers the RMSE to an extent of 22.56% and 4.32%, while decreasing the MAE by 24.84% and 5.91%, respectively. Overall, the proposed model accurately captures the characteristics of predicted cloud images and effectively improves PV power prediction accuracy. Full article

The selective wavelength absorption and scattering effects caused by complex underwater optical environments lead to a significant contradiction between color restoration and structural preservation in image enhancement. To break through this bottleneck, this paper proposes a multi-weight-guided hierarchical feature fusion framework, which transforms underwater image enhancement into a problem of optimal integration of multi-dimensional feature streams. Addressing underwater image degradation, the method constructs three complementary feature branches targeting visibility restoration, contrast enhancement, and texture compensation. Guided by multiple weights derived from Laplacian contrast, saliency, and saturation, a Laplacian and Gaussian pyramid-based multi-scale fusion strategy is designed, achieving the simultaneous preservation of global structure and enhancement of local high-frequency details. Experimental results on the SQUID real-world underwater open dataset demonstrate that, compared with eleven advanced algorithms, the proposed method exhibits high equilibrium and superiority in key metrics such as AG, IE, ENL, and UCIQE. Furthermore, its visual stability and robustness in complex and variable water environments are validated through the rank-sum composite evaluation method (RSCEM) and a refined scoring strategy. Full article

Matter in the early Universe was nearly uniform, and galaxies emerged through the gravitational growth of small primordial density fluctuations. Astrophysics has been trying to unveil the complex physical phenomena that have caused the formation and evolution of galaxies throughout the 13-billion-year history of the Universe using the first principles of physics. However, since present-day astrophysical big data contain more than 100 explanatory variables, such a conventional methodology faces limits in dealing with such data. We, instead, elucidate the physics of galaxy evolution by applying manifold learning, one of the latest methods of data science, to a feature space spanned by galaxy luminosities and cosmic time. We discovered a low-dimensional nonlinear structure of data points in this space, referred to as the galaxy manifold. We found that the galaxy evolution in the ultraviolet–optical–near-infrared luminosity space is well described by two parameters, star formation and stellar mass evolution, on the manifold. We also discuss a possible way to connect the manifold coordinates to physical quantities. Full article

Numerous insect species exhibiting positive phototaxis are strongly attracted to ultraviolet (UV) light. However, several heteropteran stink bugs, including Nezara viridula (L.) and its congener Nezara antennata Scott, show stronger attraction to traps combining UV and green light than to monochromatic UV light traps. To examine the role of visible light wavelengths in enhancing UV attraction, we evaluated the attractiveness of blue (469 nm), green (523 nm), orange (613 nm), and red (632 nm) light in combination with UV light (396–400 nm), as well as a monochromatic UV light source, under field conditions targeting Nezara bugs. Traps combining UV and blue light captured nearly three times more Nezara bugs than UV-only light traps. Conversely, traps combining orange or red and UV light captured equal to or fewer bugs than monochromatic UV light traps, indicating no enhancement in attraction with these color combinations. Furthermore, monochromatic blue light alone showed very weak attractiveness, indicating that blue light synergistically enhanced the attractiveness of UV light to bugs. Strong attractiveness to traps combining UV and green light was confirmed in the lepidopteran moth Pleuroptya ruralis (Scopoli), suggesting that multiwavelength light sources may be effective in attracting insect species beyond Heteroptera. These findings highlight the value of multiwavelength light traps, particularly traps combining UV and blue light, for improving stink bug monitoring and pest management. Full article