Search Results (145)

A new framework that can be utilized for the electrodynamics of carbon nanotubes (CNTs) with non-local surface conductivity (spatial dispersion) is presented. The model of non-local conductivity is developed on the basis of the Kubo technique applied to the Dirac equation for pseudospins. As a result, the effective boundary conditions for the electromagnetic (EM) field on a CNT surface are formulated. The dispersion relation for the eigenmodes of an infinitely long CNT is obtained and analyzed. It is shown that due to nonlocality, a new type of eigenmode is created that disappears in the local conductivity limit. These eigenmodes should be properly accounted for in the correct formulation of the CNT end conditions for the surface current, which are manifested in the EM-field scattering problem. Additional boundary conditions that consider nonlocality effects are also formulated based on the exact solution obtained for the surface current by means of using the Wiener–Hopf (WH) technique for a semi-infinite CNT. The scattering pattern of the EM-field is simulated by a finite-length model of a CNT, using a numerically solved integral equation for the surface current density and its approximate analytical solution. Thus, the scattering field of a CNT, prevailing in a wide frequency range from THz to infrared light, is analytically solved and analyzed. Potential applications for the design of nanoantennas and other electronic devices, including pointing out some future directions, are also discussed. Full article

Building outlines are important vector data for various applications, but due to the uneven point density and complex building structures, extracting satisfactory building outlines from airborne light detection and ranging point cloud data poses significant challenges. Thus, a building outline extraction method based on topology-aware loop parsing and parallel constraint is proposed. First, constrained Delaunay triangulation (DT) is used to organize scattered projected building points, and initial boundary points and edges are extracted based on the constrained DT. Subsequently, accurate semantic boundary points are obtained by parsing the topology-aware loops searched from an undirected graph. Building dominant directions are estimated through angle normalization, merging, and perpendicular pairing. Finally, outlines are regularized using the parallel constraint-based method, which simultaneously considers the fitness between the dominant direction and boundary points, and the length of line segments. Experiments on five datasets, including three datasets provided by ISPRS and two datasets with high-density point clouds and complex building structures, verify that the proposed method can extract sequential and semantic boundary points, with over 97.88% correctness. Additionally, the regularized outlines are attractive, and most line segments are parallel or perpendicular. The RMSE, PoLiS, and RCC metrics are better than 0.94 m, 0.84 m, and 0.69 m, respectively. The extracted building outlines can be used for building three-dimensional (3D) reconstruction. Full article

The modeling of light propagation in scattering media is an important topic that has attracted considerable attention in recent decades. Coupling microscopic parameters such as particle concentration, particle size, and the refractive index of the medium can broaden the applicability of the model and improve simulation accuracy. In this work, these parameters are used to regulate the anisotropy factor and the mean free path. They are then integrated into a random phase screen model constructed using the Monte Carlo and the Gerchberg–Saxton algorithm. An optical experimental setup was established, in which a Laguerre–Gaussian beam was employed as the incident light source and diffusers with mesh numbers of 1500, 600, and 220 were used as the scattering media. The model was validated through comparative analysis between simulated and experimental results. Correlation coefficients between the simulated and experimental beam profiles exceeded 0.73, and the maximum relative error in power-in-the-bucket was only 4.9%, confirming the model’s accuracy and reliability. Numerical simulations were performed based on the established model to investigate beam propagation behavior. The results indicate that increasing particle concentration and particle size both lead to enhanced beam centroid shift and beam broadening. This modeling method provides a useful tool for analyzing beam propagation in complex scattering media and holds potential applications in wavefront correction and structured beam recognition. Full article

In this work, we tested the performance of automated atmospheric PM_2.5 monitoring instruments and contrasted the data from automated measurements with those from filter-based reference measurements. The tested instruments include four brands of beta attenuation instruments (two were made in China, D1 and D2; the other two were imported from other countries, I1 and I2) and one brand of a light scattering instrument (also imported from another country, I3). The automated monitoring data were corrected based on the reference tests. The total testing period lasted 18 months. The objective of this work is to evaluate the influences of environmental factors on the performance of different automated instruments, and to improve the accuracy of the automated instruments by using a correction method. The results showed that contrasted with the reference tests, the absolute errors (MAE, mean absolute error; SD, standard deviation; and RMSE, root mean square error) of the automated monitoring instruments werehigher for temperature (T ≤ 10 °C), humidity (60% ≤ RH < 80%), and PM_2.5 concentrations (PM_2.5 ≥ 75 μg/m³). Meanwhile, the relative errors (CV, coefficient of variation; and NRMSE, normalized root mean square error) of the automated monitoring instruments were higher for humidity (RH > 80%) and PM_2.5 concentrations (PM_2.5 < 15 μg/m³). For winter data, it proved challenging to pass the reference test, which was based on a linear regression between 24-h average automated monitoring data and the integrated filter-based PM_2.5 data (aka the KBR test). Before corrections, the pass rates of D1, D2, I1, I2, and I3 in the rolling KBR tests are 57.7%, 51.3%, 41.1%, 21%, and 90.2%, respectively. After corrections, the rates increase to 79.6%, 86.6%, 81.8%, 58.9%, and 91.8%, respectively. The coefficient corrections (corrections of system errors) have made the most prominent contribution to improving the pass rates of the winter samples. The quarterly correction method can significantly improve the data accuracy of automated monitoring instruments. Full article

Aiming at the problem of image quality degradation caused by turbid water, non-uniform illumination, and scattering effect in the surface defect detection of underwater concrete structures, firstly, the concrete surface images under different shooting distances, different sediment concentrations, and different illumination conditions were collected through laboratory experiments to simulate the concrete surface images of a reservoir dam with higher sediment concentration and deeper water depth. On this basis, an underwater image enhancement algorithm named DIVE (Dynamic Illumination and Vision Enhancement) is proposed. DIVE solves the problems of luminance unevenness and color deviation in stages through the illumination–scattering decoupling processing framework, and combines efficient computing optimization to achieve real-time processing. The lighting correction of Gaussian distributions (dynamic illumination module) was processed in stages with suspended particle scattering correction (visual enhancement module), and the bright and dark areas were balanced and color offset was corrected by local gamma correction in Lab space and dynamic decision-making of G/B channel. Through thread pool parallelization, vectorization and other technologies, the real-time requirement can be achieved at the resolution of 1920 × 1080. Tests show that DIVE significantly improves image quality in water bodies with sediment concentration up to 500 g/m³, and is suitable for complex scenes such as reservoirs, oceans, and sediment tanks. Full article

Background/Objectives: Age-related macular degeneration (AMD) is a leading cause of visual impairment in older adults and often coexists with cataracts. The indication of presbyopia-correcting intraocular lenses (IOLs) in these patients remains controversial. This study aimed to evaluate the clinical performance of a non-diffractive extended depth-of-focus (EDOF) IOL (LuxSmart™) compared to a monofocal plus IOL (Tecnis Eyhance™) in cataract patients with early-stage dry AMD. Methods: In this prospective observational study, 41 patients with early-stage AMD underwent bilateral cataract surgery with either LuxSmart™ or Tecnis Eyhance™ IOL implantation, targeting postoperative emmetropia. The eye selected for analysis was the first eye scheduled for surgery. Preoperative and postoperative evaluations included high and low-contrast distance visual acuity, intermediate and near visual acuity, defocus curves, ocular light scatter (halometry), and quality of life assessment (NEI VFQ-25). Postoperative biometric accuracy and refractive outcomes were also analyzed. Results: Both IOLs showed high refractive accuracy, with 100% of eyes within ±0.50 D of target. Postoperative uncorrected distance visual acuity was 0.10 ± 0.06 LogMAR for Eyhance and 0.07 ± 0.02 for LuxSmart (p = 0.06). Low contrast VA at 20% was 0.22 ± 0.11 (Eyhance) and 0.26 ± 0.16 (LuxSmart) (p = 0.49). Depth of focus was approximately 1.75 D for both lenses. Light scatter (LDI) improved postoperatively in both groups with no significant differences (p = 0.54). VFQ-25 scores showed improvement in daily activities, though no changes were observed in driving or mental health domains. Conclusions: Both lenses are safe and effective options for early AMD patients undergoing cataract surgery, providing good functional vision at multiple distances Full article

The immersive experience of holographic displays is fundamentally limited by their space–bandwidth product (SBP), which imposes an inherent trade-off between the field of view (FOV) and eyebox size. This paper proposes a method to extend the SBP by employing cascaded modulation with a dynamic spatial light modulator (SLM) and a passive high-resolution binary random phase mask (BRPM). We find that the key to unlocking this extension of SBP lies in a sophisticated algorithmic optimization, grounded in a physically accurate model of the system. We identify and correct the Nyquist undersampling problem caused by high-frequency scattering in standard diffraction models. Based on this physically accurate model, we employ a gradient descent optimization framework to achieve efficient, end-to-end solving for complex light fields. Simulation and experimental results demonstrate that our method achieves an approximately 16-fold SBP extension (4-fold FOV) while delivering significantly superior reconstructed image quality compared to the traditional Gerchberg–Saxton (GS) algorithm. Furthermore, this study quantitatively reveals the system’s extreme sensitivity to sub-pixel level alignment accuracy, providing critical guidance for the engineering and implementation of our proposed method. Full article

The underwater environment severely degrades image quality by absorbing and scattering light. This causes significant challenges, including non-uniform illumination, low contrast, color distortion, and blurring. These degradations compromise the performance of critical underwater applications, including water quality monitoring, object detection, and identification. To address these issues, this study proposes a detail-oriented hybrid framework for underwater image enhancement that synergizes the strengths of traditional image processing with the powerful feature extraction capabilities of unsupervised deep learning. Our framework introduces a novel multi-scale detail enhancement unit to accentuate structural information, followed by a Latent Low-Rank Representation (LatLRR)-based simplification step. This unique combination effectively suppresses common artifacts like oversharpening, spurious edges, and noise by decomposing the image into meaningful subspaces. The principal structural features are then optimally combined with a gamma-corrected luminance channel using an unsupervised MU-Fusion network, achieving a balanced optimization of both global contrast and local details. The experimental results on the challenging Test-C60 and OceanDark datasets demonstrate that our method consistently outperforms state-of-the-art fusion-based approaches, achieving average improvements of 7.5% in UIQM, 6% in IL-NIQE, and 3% in AG. Wilcoxon signed-rank tests confirm that these performance gains are statistically significant (p < 0.01). Consequently, the proposed method significantly mitigates prevalent issues such as color aberration, detail loss, and artificial haze, which are frequently encountered in existing techniques. Full article

Aflatoxin B₁ (AFB₁) contamination in corn silage poses significant risks to livestock and human health. This study developed a non-destructive detection method for AFB₁ using color-sensitive arrays (CSAs). Twenty self-developed CSAs were employed to react with samples, with reflectance spectra collected using a portable spectrometer. Spectral data were optimized through seven preprocessing methods, including Standard Normal Variate (SNV), Multiplicative Scatter Correction (MSC), first-order derivative (1st D), second-order derivative (2nd D), wavelet denoising, and their combinations. Key variables were selected using five feature selection algorithms: Competitive Adaptive Reweighted Sampling (CARS), Principal Component Analysis (PCA), Random Forest (RF), Uninformative Variable Elimination (UVE), and eXtreme Gradient Boosting (XGBoost). Five machine learning models were constructed: Light Gradient Boosting Machine (LightGBM), XGBoost, Support Vector Regression (SVR), RF, and K-Nearest Neighbor (KNN). The results demonstrated significant AFB₁-responsive characteristics in three dyes: (2,3,7,8,12,13,17,18-octaethylporphynato)chloromanganese(III) (Mn(OEP)Cl), Bromocresol Green, and Cresol Red. The combined 1st D-PCA-KNN model showed optimal prediction performance, with determination coefficient ($R_p^2$ = 0.87), root mean square error (RMSEP = 0.057), and relative prediction deviation (RPD = 2.773). This method provides an efficient solution for silage AFB₁ monitoring. Full article

Coherent backscattering is an interference phenomenon that occurs in the backwards direction of the incident illumination. It arises from photons traveling the same path in opposite directions within a scattering medium. Accurately determining the background signal for normalization can be challenging in such measurements. This study investigates the use of a spatial light modulator to control spatial coherence, effectively switching the interference on and off. This approach enables independent, absolute measurements of both the signal and background across the full angular detection range without modifying the experimental setup. We demonstrate this method experimentally, highlighting the importance of accurate background determination in coherent backscattering measurements using a Fourier-based setup. Additionally, we demonstrate that measurements normalized to the correct background closely match Monte Carlo simulations of the coherent backscattering signal. Full article

Scattered light makes up a significant amount of recorded intensities during tomographic imaging, thereby leading to severe misinterpretation and artifacts in the reconstructed volume images. Correcting artificial intensities that stem from scattered light, therefore, is of primary interest and demands quantitative measurements. While numerous methods have been developed to reduce X-ray scattering artifacts, fewer methods deal with optical scattering. In this study, a measurement method for determining optical scattering in scintillators is presented with the aim of further developing correction algorithms. A theoretical model based on internal multiple reflections was developed for this purpose. This model assumes an additive exponential kernel with a certain scattering length to the system’s point spread function. This assumption was confirmed, and the scatter length was estimated from three new different kinds of experiments (hgap, rect, and LSF) on the BM18 beamline of the European synchrotron. The experiments further revealed significant differences in scattering proportion and length when different coatings are applied to the front and back faces of crystalline LuAG scintillators. Anti-reflective coatings on the backside show an effect of reducing the scattering magnitude while reflective coatings on the front side increase the proportion of the unscattered signal and, thus, show proportionally less scattering than black coating or no front coating. In particular, roughened black coating is found to worsen optical scattering. In summary, our results indicate that a combination of reflective (front) and anti-reflective (back) coatings yields the least optical scattering and, hence, the best image quality. Full article

Chemophototherapy (CPT) is an emerging cancer treatment that leverages the synergistic effects of photodynamic therapy (PDT) and chemotherapy. This approach utilizes photosensitizers like Porphyrin-Phospholipid (PoP) and combined with chemotherapeutic like Doxorubicin (Dox) to enable light-triggered drug release and targeted tumor destruction. Here, we present the validation of a wide-field laparoscopic spatial frequency domain imaging (SFDI) system in an ovarian cancer model. The system allows quantitative fluorescence imaging to obtain absolute drug concentrations in vivo to obtain the absolute concentrations of PoP and Dox fluorescence by correcting for tissue absorption and scattering effects. Fluorescence imaging revealed a significant reduction (~25%, p < 0.001) in PoP concentration in tumor regions post-illumination, demonstrating PDT-mediated photobleaching. Next, the Dox release experiment showed an increase of ~13 µg/mL Dox concentration at the local site. The ability to quantify both PoP and Dox fluorescence concentrations with a laparoscopic system underscores its potential for intraoperative monitoring of CPT efficacy. These findings indicate wide-field laparoscopic SFDI as a promising tool for guiding minimally invasive PDT and targeted drug delivery in preclinical and future clinical settings. Full article

Accurate identification of mattic epipedon degradation is critically important for addressing ecological issues such as the weakening of alpine grassland carbon sink capacity and reduced soil and water conservation. However, efficient and rapid methods for its detection remain limited. This study aimed to clarify the hyperspectral response mechanisms of mattic epipedon degradation and, based on hyperspectral technology, to construct models for identifying degraded mattic epipedon and screen preprocessing methods suitable for different moisture conditions. The results showed the following: (1) The XGBoost model with preprocessing using multiplicative scatter correction combined with second derivative transformation (MSC+SD) performed best, achieving an identification accuracy and Kappa coefficient of 0.85 and 0.82, respectively. The characteristic bands were concentrated in the visible light range (446–450 nm) and short-wave infrared range (2134 nm, 2267–2269 nm), which are closely related to the spectral responses of organic carbon and mineral components. (2) Spectral reflectance was significantly negatively correlated with moisture content, and model accuracy decreased as moisture content increased. (3) After correction using the EPO algorithm, the model accuracy for the high-moisture group improved by 13.2–16.7%, whereas that for the low-moisture group (<15%) decreased by 7.5%, verifying 15% moisture content as the critical threshold for water interference. This study elucidated the impact mechanism of moisture on the hyperspectral characteristics of the mattic epipedon. The established MSC+SD-XGBoost model adapts to varying moisture conditions, providing technical support for the rapid monitoring of mattic epipedon degradation and holding significant practical value for carbon management in alpine ecosystems. Full article

The increasing application of underwater robotic systems in deep-sea exploration, inspection, and resource extraction has created a strong demand for reliable visual perception under challenging conditions. However, image quality is severely degraded in low-light underwater environments due to the combined effects of light absorption and scattering, resulting in color imbalance, low contrast, and illumination instability. These factors limit the effectiveness of visual-based autonomous operations. We propose ATS-UGAN, a Dual-domain Adaptive Synergy Generative Adversarial Network for low-light underwater image enhancement to confront the above issues. The network integrates Multi-scale Hybrid Attention (MHA) that synergizes spatial and frequency domain representations to capture key image features adaptively. An Adaptive Parameterized Convolution (AP-Conv) module is introduced to handle non-uniform scattering by dynamically adjusting convolution kernels through a multi-branch design. In addition, a Dynamic Content-aware Markovian Discriminator (DCMD) is employed to perceive the dual-domain information synergistically, enhancing image texture realism and improving color correction. Extensive experiments on benchmark underwater datasets demonstrate that ATS-UGAN surpasses state-of-the-art approaches, achieving 28.7/0.92 PSNR/SSIM on EUVP and 28.2/0.91 on UFO-120. Additional reference and no-reference metrics further confirm the improved visual quality and realism of the enhanced images. Full article

Particulate matter affects both the light environment and air quality in greenhouses, obstructing normal gas exchange and hindering efficient physiological activities such as photosynthesis. This study focused on young Chinese cabbage (Brassica rapa L. Chinensis Group) in a greenhouse at harvest time, monitoring and comparing hyperspectral information, net photosynthetic rate, and microscopic leaf structure under two conditions: a quantitative artificial particulate matter environment and a healthy environment. Based on microscopic results combined with spectral responses and changes in photosynthetic physiological information, it is believed that particulate matter enters plant cells through stomata. Through retention and transport pathways, it disrupts the membrane structure, organelles, and other components of plant cells, resulting in adverse effects on the plant’s physiological functions. The study analyzed the mechanisms by which particulate matter influences the photosynthesis, spectral characteristics, and physiological responses of young Chinese cabbage. Physiological Reflectance Index (PRI), Modified Chlorophyll Absorption Ratio Index (MCARI), spectral red-edge position (λr), and spectral sensitive bands were used as spectral feature variables. Through cubic polynomial and 24 combinations of spectral preprocessing and modeling methods, an inversion model of spectral features and net photosynthetic rate was established. The optimal combination of spectral preprocessing and modeling methods was finally selected as SG + SD + PLS + MSC, which consists of Savitzky-Golay smooth (SG), second derivative (SD), partial least squares (PLS), and multiplicative scatter correction (MSC). The coefficient of determination (R²) of the model is 0.9513. The results indicate that particulate matter affects plant photosynthesis. The SG + SD + PLS + MSC combination method is relatively advantageous for processing the photosynthetic spectral physiological information of plants under the influence of particulate matter. The results of this study will deepen the understanding of the mechanisms by which particulate matter affects plants and provide a reference for the physiological information inversion of greenhouse vegetables under particulate matter pollution. Full article