Search Results (1,207)

Non-invasive glucose monitoring remains a persistent challenge in the scientific literature due to the complexity of biological samples and the limitations of traditional optical methods. Although advances have been made in the use of near-infrared (NIR) spectrophotometry, the direct application of the Lambert–Beer Law (LBL) to such systems has proven problematic, particularly due to the non-linear behavior observed in complex organic solutions. In this context, the objective of this work is to propose and validate a methodology for the determination of the extinction coefficient of glucose in blood, taking into account the limitations of the LBL and the specificities of molecular interactions. The method was optimized through an iterative process to provide consistent results over multiple replicates. Whole blood and plasma samples from two individuals were analyzed using spectrophotometry in the 700 nm to 1400 nm. The results showed that glucose has a high spectral sensitivity close to 975 nm.The extinction coefficients obtained for glucose (${\alpha }_g$) ranged from −0.0045 to −0.0053, and for insulin (${\alpha }_i$) from 0.000075 to 0.000078, with small inter-individual variations, indicating strong stability of these parameters. The non-linear behaviour observed in the relationship between absorbance, glucose and insulin concentrations might be explained by the changes imposed by both s and p orbitals of organic molecules. In order to make the LBL valid in this context, the extinction coefficients must be functions of the analyte concentrations, and the insulin concentration must also be a function of glucose. A regression model was found which allows to differentiate glucose from insulin concentration, by considering the cuvette thickness and sample absorbance at 965, 975, and 985 nm. It can also be concluded from experiments that wavelength of approximately 975 nm is more suitable for blood glucose calculation by using photometry. The final spectra are consistent with those reported in mid-infrared validation studies, suggesting that the proposed model encompasses the key aspects of glucose behavior in biological media. Full article

This paper presents a novel numerical approach to handling ordinary differential equations (ODEs) with initial conditions (ICs) by introducing generalized exponential Jacobi functions (GEJFs). These GFJFs satisfy the associated ICs. A crucial part of this approach is using the spectral collocation method (SCM) and building operational matrices (OMs) for the ordinary derivatives (ODs) of GEJFs. These lead to efficient and accurate computations. The suggested algorithm’s convergence and error analysis is proved. We present numerical examples to demonstrate the applicability of the approach. Full article

We report the simultaneous generation of conventional solitons (CSs) and dissipative solitons (DSs) in an erbium-doped mode-locked fiber laser with a dual-branch cavity configuration based on the nonlinear polarization rotation (NPR) technique. By incorporating fibers with different dispersion properties in two propagation branches, the laser can establish simultaneous operation in the normal and anomalous dispersion regimes within the respective loops, enabling the generation of two distinct soliton types. The CSs exhibit a 3 dB spectral bandwidth of 9.7750 nm and a pulse duration of 273 fs, while the DSs have a quasi-rectangular spectrum spanning 18.7074 nm and a pulse duration of 2.2 ps, which can be externally compressed to 384 fs. The fundamental repetition rate is approximately 21 MHz, with a repetition rate difference of 216 Hz for the two pulse trains. Stable second-order, third-order, and fourth-order harmonic mode-locking (HML) can be achieved through optimization of pump power and intracavity polarization states. The laser we build in this work has significant potential for applications in high-precision spectroscopy and asynchronous optical sampling. Full article

This work presents the development of a Z-depth system for Confocal Raman Spectroscopy (CRS), which allows for the acquisition of Raman spectra both at the surface and at depth profile in heterogeneous samples. The proposed CRS system consists of the coupling of a commercial 785 nm Raman Probe Bifurcated (RPB) with a 20x/0.40 infinity plan achromatic polarizing microscope objective, a Long Working Distance (LWD) of 1.2 cm, and a 50 μm core-multimode optical fiber used as a pinhole filter. With this implementation, it is possible to achieve both a high spatial resolution of approximately 16.2 μm and a spectral resolution of ∼14 cm⁻¹, which is determined by the FWHM of the thin 1004 cm⁻¹ Raman profile band. The system is configured to operate within 400–1800 cm⁻¹ spectral windows. The implementation of a system of this nature offers a favorable cost–benefit ratio, as commercial CRS is typically found in high-cost environments such as cosmetics, pharmaceutical, and biological laboratories. The proposed system is low-cost and employs a minimal set of optical components to achieve functionality comparable to that of a confocal Raman microscope. High signal-to-noise ratio (SNR) Raman spectra (∼660.05 at 1447 cm⁻¹) can be obtained with short integration times (∼25 s) and low laser power (30–35 mW) when analyzing biological samples such as in vivo human fingernails and fingertips. This power level is significantly lower than the exposure limits established by the American National Standards Institute (ANSI) for human laser experiments. Raman spectra were recorded from the surface of both the nails and fingertips of three volunteers, in order to characterize their biological samples at different depths. The measurements were performed in 50 μm steps to obtain molecular structural information from both surface and subsurface tissue layers. The proposed CRS enables the identification of differences between two closely spaced, centered, and narrow Raman bands. Additionally, broad Raman bands observed at the skin surface can be deconvolved into at least three sub-bands, which can be quantitatively characterized in terms of intensity, peak position, and bandwidth, as the confocal plane advances in depth. Moreover, the CRS system enables the detection of subtle, low-intensity features that appear at the surface but disappear beyond specific depth layers. Full article

Accurate identification of plastic polymers is essential for effective recycling, quality control, and environmental monitoring. This study assesses how spectral derivative preprocessing affects the classification of six common plastic polymers: Polyethylene Terephthalate (PET), Polyvinyl Chloride (PVC), Polypropylene (PP), Polystyrene (PS), and both High- and Low-Density Polyethylene (HDPE and LDPE), based on Fourier Transform Infrared (FTIR) spectroscopy data acquired at a resolution of 8 ${cm}^{-1}$. Using Savitzky–Golay derivatives (orders 0, 1, and 2), five machine learning algorithms, namely Multilayer Perceptron (MLP), Extremely Randomized Trees (ET), Linear Discriminant Analysis (LDA), Support Vector Classifier (SVC), and Random Forest (RF), were tested within a strict framework involving stratified repeated cross-validation and a final hold-out test set to evaluate generalization. The first spectral derivative notably improved the model performance, especially for MLP and SVC, and increased the stability of the ET, LDA, and RF classifiers. The combination of the first derivative with the ET model provided the best results, achieving a mean F1-score of 0.99995 (±0.00033) in cross-validation and perfect classification (1.0 in Accuracy, F1-score, Cohen’s Kappa, and Matthews Correlation Coefficient) on the independent test set. LDA also performed very well, underscoring the near-linear separability of spectral data after derivative transformation. These results demonstrate the value of derivative-based preprocessing and confirm a robust method for creating high-precision, interpretable, and transferable machine learning models for automated plastic polymer identification. Full article

The pharmacological quality of Astragalus membranaceus var. mongholicus (AMM) is determined by its bioactive compounds, and developing a rapid prediction method is essential for quality assessment. This study proposes a predictive model for AMM bioactive compounds using hyperspectral imaging (HSI) and wavelet domain multivariate features. The model employs techniques such as the first-order derivative (FD) algorithm and the continuum removal (CR) algorithm for initial feature extraction. Unlike existing models that primarily focus on a single-feature extraction algorithm, the proposed tree-structured feature extraction module based on discrete wavelet transform and one-dimensional convolutional neural network (1D-CNN) integrates FD and CR, enabling robust multivariate feature extraction. Subsequently, the multivariate feature cross-fusion module is introduced to implement multivariate feature interaction, facilitating mutual enhancement between high- and low-frequency features through hierarchical recombination. Additionally, a multi-objective prediction mechanism is proposed to simultaneously predict the contents of flavonoids, saponins, and polysaccharides in AMM, effectively leveraging the enhanced, recombined spectral features. During testing, the model achieved excellent predictive performance with R² values of 0.981 for flavonoids, 0.992 for saponins, and 0.992 for polysaccharides. The corresponding RMSE values were 0.37, 0.04, and 0.86; RPD values reached 7.30, 10.97, and 11.16; while MAE values were 0.14, 0.02, and 0.38, respectively. These results demonstrate that integrating multivariate features extracted through diverse methods with 1D-CNN enables efficient prediction of AMM bioactive compounds using HSI. Full article

Forests, as one of the most vital ecosystems on Earth, play essential roles in climate regulation, water conservation, and resource provision. However, forest health is threatened by pests, among which the larch caterpillar (Dendrolimus superans) is one of the most destructive defoliators of coniferous forests in northern China. Previous studies have mostly relied on single data sources for pest detection, which are limited by insufficient spectral information or inappropriate selection of sensitive bands, making it difficult to achieve high detection accuracy. Therefore, this study integrates hyperspectral imagery from Zhuhai-1 and multispectral imagery from Sentinel-2, leveraging their high spectral resolution and broad spectral range, thus enhancing discrimination capability. Genetic algorithm (GA) was employed to select optimal features from spectral indices, texture features, and fractional-order derivatives (FOD). Random Forest (RF), Support Vector Machine (SVM), and eXtreme Gradient Boosting (XGBoost) were compared, and model interpretability was further analyzed using Shapley additive explanations (SHAP). The results showed that XGBoost achieved the highest performance, with an overall accuracy and Kappa coefficient of 93.47% and 89.81%, demonstrating superior adaptability. Moreover, the integration of hyperspectral and multispectral data significantly improved detection accuracy compared to using either data source alone. Among the GA-selected features, Band 15 of Zhuhai-1 hyperspectral imagery exhibited strong sensitivity to pest infestation. This study provides a novel and practical approach for forest pest monitoring based on the synergistic use of hyperspectral and multispectral remote sensing data. Full article

In this study, we present the development of a high-average-power, exceptionally stable extreme-ultraviolet (EUV) laser source based on a high-order harmonic generation (HHG) technique. The spectrum of an ytterbium-doped laser is broadened through self-phase modulation (SPM) in a gas-filled hollow fiber and compressed down to 25.3 fs for efficient harmonic generation. The high harmonics are generated in a krypton (Kr) gas cell, delivering a total power of 241 μW within the 30–60 nm spectral range, corresponding to a single harmonic output of 166 μW at a central wavelength of 46.8 nm. Notably, the system demonstrates good power stability with a root-mean-square (RMS) deviation of only 1.95% over 12 h of continuous operation. This advanced light source holds great potential for applications in nano- and quantum-material development and in semiconductor wafer defect detection. Future work aims to further enhance the output power in the 30–60 nm band to the milliwatt level, which would significantly bolster scientific research and technological development in related fields. Full article

This paper proposes an integrated OFDM signal system combining sidetone signals for communication and measurement, addressing the challenges of system complexity, resource waste, and interference caused by separated communication and measurement functions in traditional LEO satellite systems. The proposed approach effectively combines sidetone signals with OFDM technology, utilizing different short-period coprime pseudorandom codes as pilots to form composite ranging codes, while inserting multi-frequency sidetone signals at specific subcarrier points for precise ranging. A dual-mode channel estimation algorithm is designed to merge the channel estimation results from ranging pilots and sidetone signals, significantly enhancing system performance. Additionally, an adaptive ranging mode switching mechanism based on error thresholds achieves dynamic balance between ranging accuracy and spectral efficiency. Simulation results demonstrate that the proposed system can reduce bit error rate to approximately 10⁻³ at 6 dB SNR, saving about 3 dB of transmission power compared to conventional pilot methods, while achieving centimeter-level ranging accuracy of approximately 0.02 m, improving precision by 3–4 orders of magnitude over traditional pilot methods. The proposed scheme provides a high-precision, high-efficiency integrated solution for LEO satellite communication systems. The theoretical performance assumes idealized conditions, with practical deployment requiring comprehensive error modeling for hardware imperfections and environmental variations. Full article

This study, leveraging near-infrared spectroscopy technology and integrating vegetation index analysis, aims to develop a hyperspectral imaging-based non-destructive inspection technique for swift monitoring of crop chlorophyll content by rapidly predicting leaf SPAD. To this end, a high-precision spectral prediction model was first established under laboratory conditions using ex situ lyophilized Leaf samples. This model provides a core algorithmic foundation for future non-destructive field applications. A systematic study was conducted to develop prediction models for leaf SPAD values of Korla fragrant pear at different growth stages (fruit-setting period, fruit swelling period and Maturity period). This involved comparing various spectral preprocessing algorithms (AirPLS, Savitzky–Golay, Multiplicative Scatter Correction, FD, etc.) and CARS Feature Selection methods for the screening of optimal spectral feature band. Subsequently, models were constructed using BP Neural Network and Support Vector Regression algorithms. The results showed that leaf samples at different growth stages exhibited significant differences in their spectral features within the 5000–7000 cm⁻¹ (effective features for predicting chlorophyll (SPAD)) and 7000–8000 cm⁻¹ (moisture absorption valley) bands. The Savitzky–Golay+FD (Savitzky–Golay smoothing combined with first-order derivative (FD)) preprocessing algorithm performed optimally in feature extraction. Growth period specificity models significantly outperformed whole growth period models, with the optimal models for the fruit-setting period and fruit swelling period being FD-CARS-BP (Coefficient of determination (R²) > 0.86), and the optimal model for the Maturity period being Savitzky–Golay-FD+Savitzky–Golay-CARS-BP (Coefficient_of_determination (R²) = 0.862). Furthermore, joint modeling of characteristic spectra and vegetation indices further improved prediction performance (Coefficient of determination (R²) > 0.85, Root Mean Square Error (RMSE) 2.5). This study presents a reliable method for non-destructive monitoring of chlorophyll content in Korla fragrant pears, offering significant value for nutrient management and stress early warning in precision agriculture. Full article

During operation, the voltage and current waveforms output by pulse width modelation (PWM) inverters often contain high-frequency ripples. Compared to the average model, the generalized average model (GAM) can take into account the effects of high-frequency components and harmonics, further improving the accuracy of the model calculations. However, as the order of GAM increases, the construction of its mathematical model becomes increasingly complex and may lose the original harmonic characteristics of the system. To facilitate the analysis of the influence of the order of the generalized average model on the harmonic characteristics of its original system, a GAM of the PWM inverter was constructed using the Modelica language based on the mapping rules from the time-domain state-space model to the multi-frequency-domain GAM. Subsequently, based on the spectral distribution of the external control signal, a configuration strategy for the characteristic harmonic frequencies of the GAM was proposed. Simulation experiments were conducted separately for one-phase and three-phase inverters. The results indicate that the proposed configuration strategy for the characteristic harmonic frequencies of GAM not only preserves the harmonic characteristics of the original system but also improves the computational efficiency of the system model. Full article

The study of new nanoscopic heterostructures composed of different materials follows the idea that the presence of boundary conditions, interfaces and combinations of materials will produce appropriate spectral properties or quantum states, resulting in new devices. Here, we present a detailed study of two kinds of nanowires formed using topological insulators. First, we consider cylindrical nanowires with a cylindrical core of constant radius along the wire, covered by a shell of uniform width. The core and the shell materials are different topological insulators. We thoroughly study the spectra of distinct wires, considering combinations of materials and sizes of the core and shell radii. We also study the expectation values of the spin operators. Then, we consider wires with only a cylindrical shell. For this case, we pay special attention to the limit when the width of the shell is approximately an order of magnitude smaller than the inner and outer radii of the shell. As we use a high-accuracy variational method to obtain the spectra and quantum states, we also study information-like quantities such as the fidelity and quantum entropy of the topological and normal states of the wires. Full article

Fiber-optic temperature sensors with advantages such as simplicity, low cost, and high sensitivity have attracted increasing attention. In this work, we propose a self-adaptive polymer Fabry–Pérot interferometer (PFPI) sensor for ultrasensitive and wide-linear-range thermal sensing. This design achieves a temperature sensitivity of 0.95 nm/°C, representing an enhancement of two orders of magnitude compared to conventional fiber Bragg gratings. To address the challenge of spectral shifts exceeding the free spectral range due to the high sensitivity, a local cross-correlation algorithm is introduced for accurate wavelength tracking. We demonstrate ultrahigh-resolution (0.025 °C) scanning thermal field imaging and sensitive human physiological monitoring, including precise body temperature and respiratory rate detection. These results highlight the dual capability of our PFPI sensor for both microscopic thermal mapping and non-invasive healthcare applications. Full article

Sparse arrays can effectively reduce antenna cost and implementation complexity. However, most existing research in sparse array design mainly focuses on far-field scenarios, which cannot be directly applied to near-field (NF) source localization, where the delay term and source incident parameters exhibit a nonlinear relationship. In this paper, employing a symmetric enhanced nested array, a high-precision underdetermined three-dimensional (3D) NF localization method is proposed. Firstly, the symmetry of the array and the fourth-order cumulant are utilized to construct the equivalent virtual far-field (FF) received data. Then, a gridless, sparse, and parametric approach combined with an l₁-singular value decomposition-based pairing procedure is employed to obtain estimates of two paired angles. Finally, a one-dimensional (1D) spectral estimator is applied to obtain the estimate of the range parameter. By analyzing the virtual aperture, the optimal parameter configuration for a given number of elements is obtained. As shown by simulation results, the proposed method can handle underdetermined estimation. Compared with the other algorithms, the proposed algorithm achieves significant improvements in both angular and distance accuracy, with enhancements of 65% and 61.7%, respectively. Full article

To enhance the spectral efficiency of hybrid beamforming in millimeter-wave massive MIMO systems, the problem is formulated as a high-dimensional non-convex optimization under constant modulus constraints. A novel algorithm based on fourth-order tensor Tucker decomposition is proposed. Specifically, the frequency-domain channel matrices are structured into a fourth-order tensor to explicitly capture the couplings across the spatial, frequency, and user domains. To tackle the non-convexity induced by constant modulus constraints, the analog precoder and combiner are derived by solving a truncated-rank Tucker decomposition problem through the Alternating Direction Method of Multipliers and Alternating Least Squares schemes. Subsequently, in the digital domain, the Regularized Block Diagonalization algorithm is integrated with the subcarrier and user factor matrices—obtained from the tensor decomposition—along with the water-filling strategy to design the digital precoder and combiner, thereby achieving a balance between multi-user interference suppression and noise enhancement. The proposed tensor-based algorithm is demonstrated through simulations to outperform existing state-of-the-art schemes. This work provides an efficient and mathematically sound solution for hybrid beamforming in dense multi-user scenarios envisioned for sixth-generation mobile communications. Full article