Search Results (319)

This paper systematically analyzes the propagation, transformation, and accumulation mechanisms of multi-source noise and device non-idealities within the complete signal chain from the transmitter through the channel to the receiver, focusing on wireless optical coherent communication systems from a signal propagation perspective. It establishes the stepwise propagation process of signals and noise from the transmitter through the atmospheric turbulence channel to the coherent receiver, clarifying the coupling mechanisms and accumulation patterns of various noise sources within the propagation chain. From a signal propagation viewpoint, the study focuses on analyzing the impact mechanisms of factors, such as Mach–Zehnder modulator nonlinear distortion, atmospheric turbulence effects, 90° mixer optical splitting ratio imbalance, and dual-balanced detector responsivity mismatch, on system bit error rate performance and constellation diagrams under conditions of coexisting multiple noises. Simultaneously, by introducing differential and common-mode processes, the propagation and suppression characteristics of additive noise at the receiver end within the balanced detection structure were analyzed, revealing the dominant properties of different noise components under varying optical power conditions. Simulation results indicate that within the range of weak turbulence and engineering parameters, the impact of modulator nonlinearity on system bit error rate is relatively minor compared to channel noise. Atmospheric turbulence dominates system performance degradation through the combined effects of amplitude fading and phase perturbation, causing significant constellation spreading. Imbalanced optical splitting ratios and mismatched responsivity at the receiver weaken common-mode noise suppression, leading to variations in effective signal gain and constellation stretching/distortion. Under different signal light power and local oscillator light power conditions, the system noise exhibits distinct dominant characteristics. Full article

The transition to the 4th Industrial Revolution (4IR) in the electroplating industry necessitates intelligent, real-time monitoring systems to replace traditional, time-consuming offline analysis. In this study, we developed a cost-effective, automated measurement system for hexavalent chromium (Cr(VI)) in plating wastewater using an Arduino-based RGB sensor. Unlike conventional single-variable approaches, we conducted a comprehensive feature sensitivity analysis on multi-sensor data (including pH, ORP, and EC). While electrochemical sensors were found to be susceptible to pH interference, the analysis identified that the Red and Green optical channels are the most critical indicators due to the distinct chromatic characteristics of Cr(VI). Specifically, the combination of these two channels effectively functions as a dual-variable sensing mechanism, compensating for potential interferences. To optimize prediction accuracy, a systematic machine learning strategy was employed. While the Convolutional Neural Network (CNN) achieved the highest classification accuracy of 89% for initial screening, a polynomial regression algorithm was ultimately implemented to model the non-linear relationship between sensor outputs and concentration. The derived regression model achieved an excellent determination coefficient (R² = 0.997), effectively compensating for optical saturation effects at high concentrations. Furthermore, by integrating this sensing model with the chemical stoichiometry of the reduction process, the proposed system enables the precise, automated dosing of reducing agents. This capability facilitates the establishment of a “Digital Twin” for wastewater treatment, offering a practical ICT (Information and Communication Technology)-based solution for autonomous process control and strict environmental compliance. Full article

Lately the nonlinear optical third-harmonic generation (THG) microscopy is starting to emerge as a laboratory standard for label-free studies in biological samples. In this study, the THG signals produced from corn starch granules are investigated. In particular, the polarization-dependent THG (P-THG) signals emerging from the outer layer (shell) of the starch granules are compared with the P-THG signals originating from their inner portion (core). By rotating the linear polarization of the excitation beam, two distinct P-THG modulation patterns are revealed within single granules, corresponding to their shells and to their structurally different cores. These patterns are analyzed using a theoretical framework that describes THG from an orthorhombic crystal symmetry, characteristic of corn starch. This allows us to extract point-by-point in the granules the ratios of the ${\chi }^{\left(3\right)}$ susceptibility tensor elements and the average molecular orientations. Then, the anisotropy ratio (AR = ${\chi }_{xxxx}^{\left(3\right)}$/${\chi }_{yyyy}^{\left(3\right)}$) is defined and used as a quantitative descriptor of the local molecular arrangements. Our results show that the shells and cores exhibit distinct AR values, probing the anisotropy in the molecular arrangements between the two regions. This study establishes P-THG as a powerful contrast mechanism for probing structural anisotropy in biological samples beyond conventional THG intensity-only microscopy. Full article

To overcome the limitations of existing automatic modulation classification (AMC) methods that mainly target single-signal scenarios in radio-over-fiber (RoF) system, a mixed-signal AMC scheme based on phase noise-insensitive high-order cumulants (PNI-HOC) and distribution characteristics is proposed. The approach enables accurate classification of mixed signals in RoF system. Specifically, a PNI-HOC algorithm is first introduced to mitigate the influence of laser linewidth-induced phase noise. Then, distribution characteristics derived from the signal amplitude histogram are extracted to construct a two-dimensional characteristics space. These characteristics are subsequently fed into decision tree and support vector machine (SVM) classifiers for signal identification. To validate the effectiveness of the scheme, a 10 GBaud RoF system with a 70 km fiber link is implemented. The simulation results show that, compared with the conventional high-order cumulant method, the approach solely based on amplitude histogram distribution characteristics and the scheme based on deep neural networks (DNN) classifier using histogram characteristics, the proposed scheme achieves significantly higher classification accuracy at low optical signal–noise ratios (OSNRs). In particular, when the fiber length is 70 km and the OSNR is ≥16 dB, the classification accuracy of mixed signals is consistently maintained at 100%. Furthermore, the robustness of the proposed method is verified under various system impairments, including laser phase noise, chromatic dispersion and nonlinear effects, amplified spontaneous emission noise, multipath fading, etc., confirming its superior and stable performance. Full article

Cu₂ZnSnSe₄ is a promising light-absorbing material for cost-effective and eco-friendly thin-film solar cells; however, its synthesis often leads to secondary phases that limit device efficiency. To overcome these challenges, we devised a straightforward and efficient method to obtain single-phase Cu₂ZnSnSe₄ nanocrystalline powders directly from the elements Cu, Zn, Sn, and Se via mechanochemical synthesis followed by vacuum annealing at 450 °C. Phase evolution monitored by X-ray diffraction (XRD) and Raman spectroscopy at two-hour milling intervals confirmed the formation of phase-pure kesterite Cu₂ZnSnSe₄ and enabled tracking of transient secondary phases. Raman spectra revealed the characteristic A₁ vibrational modes of the kesterite structure, while XRD peaks and Rietveld refinement (χ² ~ 1) validated single-phase formation with crystallite sizes of 10–15 nm and dislocation densities of 3.00–3.20 10¹⁵ lines/m². Optical analysis showed a direct bandgap of ~1.1 eV, and estimated linear and nonlinear optical constants validate its potential for photovoltaic applications. Scanning electron microscopy (SEM) analysis showed uniformly distributed particles 50–60 nm, and energy dispersive X-ray (EDS) analysis confirmed a near-stoichiometric Cu:Zn:Sn:Se ratio of 2:1:1:4. X-ray photoelectron spectroscopy (XPS) identified the expected oxidation states (Cu⁺, Zn²⁺, Sn⁴⁺, and Se²⁻). Electrical characterization revealed p-type conductivity with a mobility (μ) of 2.09 cm²/Vs, sheet resistance (ρ) of 4.87 Ω cm, and carrier concentrations of 1.23 × 10¹⁹ cm⁻³. Galvanostatic charge–discharge testing (GCD) demonstrated an energy density of 2.872 Wh/kg⁻¹ and a power density of 1083 W kg⁻¹, highlighting the material’s additional potential for energy storage applications. Full article

Second harmonic generation measurements for neodymium-doped bismuth–tellurium borate (Bi₃TeBO₉:Nd³⁺) powders are shown for the first time. Using undoped and low-content Nd³⁺-doped samples associated with the strongest nonlinear optical response, studies of temperature-dependent second-harmonic generation near the absorption edge were conducted. Spectroscopic measurements of the investigated powders revealed characteristic Nd³⁺ absorption bands and helped to estimate the corresponding energy band gaps for the chosen samples. The influence of low Nd³⁺-content on the absorption edge shift, as well as on the enhancement of second-harmonic generation and its temperature attenuation, is discussed. Temperature-dependent X-ray diffraction measurements enabled researchers to calculate the thermal expansion coefficients for undoped and Nd³⁺-doped Bi₃TeBO₉ and to assess the impact of this phenomenon on its acentricity. Thermogravimetric studies demonstrated the absence of phase transitions for the chosen samples up to their incongruent melting points. Energy Dispersive X-ray Spectroscopy measurements verified the uniformity of Nd³⁺ distribution in doped Bi₃TeBO₉ powders. The suitability of polycrystalline Bi₃TeBO₉:Nd³⁺ as media for the self-frequency doubling devices for potential optoelectronic and biomedical applications was assessed. The finest fractions of deagglomerated and suspended powders were extracted and demonstrated near-nanostructural morphology of separated particles, as revealed by means of atomic force microscopy. Full article

Video frame interpolation in ultra-high-definition extreme motion scenes remains highly challenging due to large displacements, nonlinear motion, and occlusions that disrupt spatio-temporal symmetry. To address this issue, this study proposes a frame interpolation method for extreme motion scenes based on dual alignment and region-adaptive interaction from the perspectives of cross-frame localization and adaptive reconstruction. Specifically, we design a two-stage motion information alignment strategy that obtains two types of motion information via optical flow estimation and offset estimation, and it progressively guides reference pixels for accurate long-range cross-frame localization, mitigating structural misalignment caused by limited receptive fields while simultaneously alleviating spatiotemporal asymmetry caused by inconsistent inter-frame motion speed and direction. Based on this, we introduce a region-adaptive interaction module that automatically adapts motion representations for different regions through cross-frame interaction and leverages distinct attention pathways to accurately capture both the global context and local high-frequency motion details. This achieves a dynamic feature fusion tailored to regional characteristics, significantly enhancing the model’s ability to perceive the overall structure and texture details in extreme motion scenarios. In addition, the introduction of a motion compensation module explicitly captures pixel motion relationships by constructing a global correlation matrix that compensates for the positioning errors of the dual alignment module in extreme motion or occlusion areas. The experimental results demonstrate that the proposed method achieves excellent overall performance in ultra-high-definition extreme motion scenes, with a PSNR improvement of 0.05 dB over state-of-the-art methods. In multi-frame interpolation tasks, it achieves an average PSNR gain of 0.31 dB, demonstrating strong cross-scene interpolation capability. Full article

Ultrashort fiber lasers are one of the current research hotspots in the field of lasers. They have the advantages of compact structure and high beam quality. Passively mode-locking using saturable absorbers (SAs) is an important scheme for generating picosecond and femtosecond pulses. A deep understanding of the passive mode-locking mechanism is key to maturing ultrafast laser technology. In recent years, the passively mode-locking technology of SAs has been improved in material systems, device preparation, and cavity structures. SAs are primarily divided into artificial SAs and real SAs. Real SAs primarily include semiconductor saturable absorption mirrors (SEASAMs) and nanomaterials. Artificial SAs primarily include nonlinear optical loop mirrors (NOLMs), nonlinear multimode interference (NLMMI), nonlinear polarization rotation (NPR), and the Mamyshev oscillator. Herein, we mainly review passively mode-locked fiber lasers employing various SAs, as well as their working principles and technical characteristics. By focusing on the representative achievements, the developmental achievements of ultrafast lasers based on SAs are demonstrated. Finally, the prevailing challenges and promising future research directions in SA’s mode-locking technology are discussed. Full article

In this paper, we investigate the physical mechanics of vibration wave detection in distributed acoustic sensing (DAS) systems with the aim of enhancing the interpretation of the quantitative wavefield. We investigate the nonlinear relationship of DAS gauge length and pulse width on the seismic wave response, and the result is explained by the trigonometric relationship of backscattered Rayleigh wave phases. We further demonstrate the influence of spiral winding on DAS performance and also build phase response models for P-waves and S-waves in helically wound cables. These models suggest that the winding angle controls the measurement interval spacing and the angle of wave incidence. Additionally, integration of structural reinforcement improves the amplitude response characteristics and SNR. The experimentally inspired results show, using simulations and field tests, that the same vibration sources can give helically wound cables with larger winding angles the largest phase amplitudes, which would substantially exceed that of straight cables. SNR increased significantly (approximately 10% to 30%). The efficacy of the method was also checked using experiments for different vibration amplitudes and frequencies. Such results provide evidence for the design and installation of fiber-optic cables for use in practical engineering applications involving safety monitoring. Full article

Visible light positioning (VLP) has emerged as a promising indoor positioning technology, owing to its high accuracy and cost-effectiveness. In practical scenarios, signal attenuation, multiple light reflections, or light-deficient regions, particularly near room corners or furniture, can significantly degrade the light quality. In addition, the non-uniform light distribution by light-emitting diode (LED) luminaires can also introduce errors in VLP estimation. To mitigate these challenges, recent studies have increasingly explored the use of machine learning (ML) techniques to model the complex nonlinear characteristics of indoor optical channels and improve VLP performance. Convolutional neural networks (CNNs) have demonstrated strong potential in reducing positioning errors and improving system robustness under non-ideal lighting conditions. However, the performance of CNN-based systems is highly sensitive to their hyperparameters, including learning rate, dropout rate, batch size, and optimizer selection. Manual tuning of these parameters is not only time-consuming but also often suboptimal, particularly when models are applied to new or dynamic environments. Therefore, there is a growing need for automated optimization techniques that can adaptively determine optimal model configurations for VLP tasks. In this work, we propose and demonstrate a VLP system that integrates received signal strength (RSS) signal pre-processing, a CNN, and particle swarm optimization (PSO) for automated hyperparameter tuning. In the proof-of-concept VLP experiment, three different height layer planes (i.e., 200, 225, and 250 cm) are employed for the comparison of three different ML models, including linear regression (LR), an artificial neural network (ANN), and a CNN. For instance, the mean positioning error of a CNN + pre-processing model at the 200 cm receiver (Rx)-plane reduces from 9.83 cm to 5.72 cm. This represents an improvement of 41.81%. By employing a CNN + pre-processing + PSO, the mean error can be further reduced to 4.93 cm. These findings demonstrate that integrating PSO-based hyperparameter tuning with a CNN and RSS pre-processing significantly enhances positioning accuracy, reliability, and model robustness. This approach offers a scalable and effective solution for real-world indoor positioning applications in smart buildings and Internet of Things (IoT) environments. Full article

This study investigates the convective–diffusive Cahn–Hilliard equation, a nonlinear model which is used in real-world applications to phase separation and material pattern formation. Using the modified Sardar sub-problem technique, which is an extension of the Sardar sub-equation approach, we derive multiple classes of exact soliton solutions, including bright, dark, kink, and periodic forms. The parametric behaviors of these solutions are examined and visualized through analytical plots generated in Mathematica and Maple. Furthermore, UV–Vis spectrophotometry is employed to examine the optical response of copper oxide (CuO) thin films. The films exhibited a sharp absorption edge around 380–410 nm and an optical band gap of approximately 2.3 eV, confirming their semiconducting nature. The experimentally observed periodic transmission characteristics are linked with the theoretical soliton profiles predicted by the model. Overall, the proposed analytical and experimental framework establishes a clear connection between nonlinear wave theory and thin-film optical characterization, providing new insights into soliton transformation phenomena in complex material systems. Full article

The need for sustainable alternatives to petroleum-based plastic packaging has prompted interest in biodegradable biopolymer films. This study developed edible composite films using minimally refined chitosan from American lobster (Homarus americanus) shell waste combined with fish gelatin, glycerol, and sunflower oil. A Box–Behnken design within a response surface methodology (RSM) framework was used to investigate the effects of these formulation variables on ten key film properties, including mechanical strength, water sensitivity, barrier performance, and optical characteristics. High-quality empirical models (R² ≥ 0.88) captured nonlinear, synergistic, and antagonistic interactions among the components, revealing trade-offs between competing attributes. Simultaneous multi-response optimization identified balanced formulations suited to various food packaging needs, including perishable, fresh, and dry products. Experimental validation of selected formulations confirmed model predictions within 5% error under laboratory conditions. Up to 68% of the inhibition activity against Escherichia coli was retained in a few composite formulations when compared with neat chitosan films, thus supporting their potential for active packaging. The key highlight of the present work is the use of crude chitosan derived from lobster shell waste, a low-cost, sustainable alternative to highly purified commercial sources, demonstrating the practical viability of marine byproduct valorization. Overall, this study advances the development of high-performance, application-specific biopolymer films and highlights RSM as an effective tool for optimizing multifunctional edible packaging materials. Future work should focus on enhancing antimicrobial functionality, evaluating real-world performance, and assessing consumer acceptance to support industrial adoption. Full article

Numerous machine learning models have been widely used for the spatial prediction of PM_2.5 mass concentrations in the field of remote sensing, but most studies rely on single models, limiting their ability to capture complex nonlinear relationships. Furthermore, traditional Aerosol Optical Depth (AOD) methods suffer from extensive missing values due to algorithmic limitations, hindering daily PM_2.5 mass concentration retrieval. This study first developed a hybrid random forest and extreme gradient boosting model (RF-XGBoost) to overcome single-model accuracy constraints. Subsequently, Top-of-Atmosphere (TOA) reflectance replaced conventional AOD as the hybrid model’s input. Finally, we integrated four-year (2020–2023) TOA reflectance, normalized difference vegetation index (NDVI) data, meteorological data, digital elevation model (DEM) data, and day-of-year data to develop a high-precision hybrid model specifically optimized for Jiangxi Province. The simulation results demonstrated that the hybrid RF-XGBoost model (test-R² = 0.82, RMSE = 7.25 μg/m³, MAE = 4.90 μg/m³) outperformed the single Random Forest Model by 25% and 26% in terms of the root mean square error (RMSE) and mean absolute error (MAE), respectively. The high predictive accuracy of our method confirms its effectiveness in generating reliable PM_2.5 estimates. The resulting four-year dataset also successfully delineated the characteristic seasonal PM_2.5 pattern in the region, with the highest levels in winter and the lowest in summer, alongside a clear decreasing annual trend, signifying gradual atmospheric improvement. Full article

This study employs density functional theory (DFT) and time-dependent DFT (TD-DFT) to systematically investigate the ground- and excited-state properties of hybrid systems composed of CdSe quantum dots (QDs) with graphene and boron nitride (BN). Through Multiwfn wavefunction analysis, we calculated the highest occupied molecular orbital–lowest unoccupied molecular orbital (HOMO–LUMO) gaps and density of states (DOS), revealing distinct symmetry-dependent electronic characteristics. The HOMO–LUMO gap analysis demonstrates graphene’s superior charge transfer capability compared to BN, attributed to its higher structural symmetry enabling more efficient orbital overlap. DOS analysis further confirms the enhanced electrical conductivity in symmetry-matched graphene hybrids. The independent gradient model (IGM) and reduced density gradient (RDG) analyses reveal fundamentally different interfacial interaction patterns: the graphene hybrid exhibits uniform van der Waals interactions, consistent with its hexagonal symmetry, while the BN system shows heterogeneous interactions with localized hydrogen bonding due to symmetry reduction from heteroatomic composition. Binding energy calculations indicate greater stability in the graphene-based hybrid, reflecting optimal symmetry matching at the interface. UV–Vis spectra analysis shows that graphene dominates the optical response in its hybrid system, maintaining its symmetric spectral characteristics, while CdSe QDs govern the BN hybrid’s absorption. Electrostatic potential distributions remain essentially unchanged post-hybridization, preserving the intrinsic charge symmetry of components. Two-photon absorption (TPA) characterization reveals significant nonlinear optical properties in CdSe QDs, particularly at the first excited state. This work provides the first systematic comparison of charge transfer dynamics in CdSe QDs hybridized with graphene versus BN, demonstrating how material symmetry governs optoelectronic modulation mechanisms. The findings establish symmetry–property relationships that inform the design of low-dimensional hybrid materials for photonic applications. Full article

Generation of idler pulses via the four-wave mixing (FWM) effect in a nonlinear optical ring resonator (NORR) was investigated numerically. It was found that the relative delay $\tau$ between input pump pulses significantly affects both the energy and temporal–spectral characteristics of generated idler pulses. Specifically, idler pulse energy decreases with increasing $\tau$ due to phase-matching conditions in FWM. Maximum energy transfer occurs for $\tau$ = 0, where optimal phase alignment among pump, signal, and idler waves is achieved. Temporal and spectral analysis at different relative delays reveals a change from a symmetric, multi-subpulse structure with a comb-like spectrum (for $\tau$ = 0) to a near-single-pulse form with a single-comb-line spectrum (for $\tau$ = 9 ps). These findings demonstrate the critical dependence of FWM efficiency on pump pulse synchronization. Therefore, precise control of the relative delay is essential for optimizing idler pulse generation in NORRs. Full article