Search Results (442)

In response to the need for a simple, non-destructive method for evaluating avocado ripeness, we measured chlorophyll-related fluorescence and chromaticity of the outer skin using simple optical equipment and evaluated their relationship with whole-fruit compression (wfc), which was used as a firmness-based ripeness index. A compact system consisting of a blue LED excitation source and a small spectrometer was used to measure fluorescence spectra, and a commercially available colorimeter was used to evaluate chromaticity. Hass avocado samples purchased from multiple retail stores in Japan and stored for different periods were examined. The combination of the fluorescence intensity ratio I₇₄₀/I₆₈₅ and the lightness parameter L* showed a moderate correlation with wfc, with R² = 0.48. The fluorescence ratio I₇₄₀/I₆₈₅ was treated not as a direct measure of chlorophyll content, but as a spectral index associated with ripening-related changes in avocado skin, including chlorophyll-related fluorescence and skin optical properties. These results suggest that the combination of simple blue LED-excited fluorescence and chromaticity measurements may be useful as a practical screening approach for roughly estimating avocado ripeness in commercially available fruit. Full article

Deploying medical visual question answering (VQA) systems over wireless networks introduces a fundamental challenge: channel-induced image degradation may corrupt the visual representations extracted by large vision-language models (VLMs), leading to unreliable diagnostic decisions. We investigate the robustness of frozen LLaVA-1.6, BLIP-2, and BioViL-T hidden-state features under additive white Gaussian noise (AWGN), Rayleigh fading, and six combined JPEG-compression-plus-channel conditions (quality factors $q\in \{20,50,70\}$) across signal-to-noise ratios (SNRs) from $-5$ to $+20$ dB. A lightweight MLP classifier is trained exclusively on clean features and evaluated on channel-degraded features, enabling controlled analysis of representation robustness without retraining. We introduce the Feature Robustness Score (FRS), defined as the difference between cosine similarity and normalized L2 drift of clean versus degraded features, together with a validation-set FRS threshold analysis as a label-free retraining criterion. A wavelet sub-band energy analysis further characterizes the spectral distribution of channel-induced feature drift. Experiments on PathVQA and VQA-RAD reveal four key findings: (1) LLaVA-1.6 features maintain cosine similarity above $0.98$ across all eight channel conditions and all SNR levels, with statistically significant MLP gains at every tested point ($p<0.05$, McNemar’s test); (2) BLIP-2 and BioViL-T features are less stable but still support consistent MLP improvements, with BioViL-T performing competitively on VQA-RAD, suggesting domain alignment matters; (3) JPEG compression quality ($q=20,50,70$) has negligible impact on feature drift, establishing VLM features as JPEG quality-invariant; and (4) wavelet analysis confirms that channel noise primarily affects high-frequency detail bands while preserving low-frequency semantic content. Full article

We demonstrate an 80 MHz, 350 mW, 120 fs, 770 nm femtosecond laser based on a nonlinear compressed 1540 nm femtosecond fiber laser. The home-built 1540 nm fiber laser, delivering 80 MHz, 2.69 W, 269 fs laser pulses, was realized by employing spectral pre-modulation and pre-chirp management inside an Er/Yb co-doped fiber power amplifier. The subsequent nonlinear fiber pulse compression stage was utilized to further nonlinearly compress the pulse duration to 128 fs based on the Gaussian assumption. Detailed numerical simulation was also implemented to investigate the optical dynamics of the nonlinear compression process. Finally, a 0.5 mm thick fan-out periodically poled lithium niobate (PPLN) crystal was utilized to generate the frequency-doubled, 350 mW, 770 nm laser pulses with a 120 fs pulse duration based on the Gaussian assumption. Full article

Compressed sensing (CS) enables signal reconstruction from fewer measurements when the signal is sparse in a transform domain. However, executing ${\ell }_1$-regularized recovery on MCU-class hardware is challenging due to limited compute resources and the cost of repeated forward and adjoint operator evaluations. This paper presents a band-limited proximal variant of FISTA that enforces known spectral support during thresholding, restricting the effective optimization domain without changing the measurement model. We implement a complete CS reconstruction pipeline on an STM32F407 (Cortex-M4) using CMSIS-DSP FFT/IFFT kernels and evaluate it using ECG waveforms acquired through an AD8232 front end as benchmark signals. With $M=340$ measurements (33% of uniform sampling), the embedded implementation achieves a PRDN of 24.38%, closely matching MATLAB references (CVX: 22.64%, FISTA: 22.39%) under identical hyperparameters. Cycle-accurate profiling shows that FFT/IFFT-based forward/adjoint operators dominate the per-iteration runtime. Under a 60 Hz band-limited setting, the required iterations are reduced from 30 to 16 with an acceptable PRDN, demonstrating a practical trade-off between reconstruction accuracy and computational cost on MCU-class devices. Full article

Waveform agility improves anti-reconnaissance and anti-jamming capability in diverse inverse synthetic aperture radar (ISAR) scenarios, but it also breaks the phase variation assumptions used for conventional coherent processing. For cooperative bistatic ISAR radars, the problem is further complicated by the bistatic geometry and phase evolution induced by synchronization. This paper develops a joint coherent integration method for a cooperative bistatic radar with simultaneous pulse width (PW) and pulse repetition interval (PRI) agility. Firstly, we establish and analyze a bistatic geometric model to reveal key integration problems under agile waveforms, and then derive the coherent processing interval (CPI) local polynomial description for bistatic delay, Doppler and acceleration. On this basis, the matched filter response of each agile pulse is analyzed under the fixed-bandwidth assumption with linear frequency modulation (LFM), showing that PW agility produces a compressed peak displacement and an additional deterministic phase term, whereas PRI agility converts slow-time coherent integration into a nonuniformly sampled spectral estimation problem. To solve this problem, a joint fast and slow-time compensation route is derived, together with a bistatic-specific parameter design method that connects coherent integration tolerances with the bistatic angle and the observable projection vector. Finally, we test the performance of the proposed joint integration method in multiple scenarios and verify its effectiveness and robustness, which enhances detection performance and resolution for target localization. Full article

This paper proposes and experimentally demonstrates a fiber ring laser (FRL) salinity sensing system based on a Sagnac loop (SL) formed by a tapered polarization-maintaining fiber (TPMF). The operating principle is that salinity modulates the birefringence of the polarization-maintaining fiber (PMF), causing a shift in the interference wavelength of the SL transmission spectrum, while the FRL narrows the optical spectrum and enhances the signal-to-noise ratio (SNR). In the experiment, the SL consists of a 20-cm-long PMF with a tapered waist diameter of 10.86 μm. Over the salinity range of 0‰ to 30‰, the sensitivity of the laser-based sensing system is 97 pm/‰, which agrees well with the 93 pm/‰ sensitivity obtained using a broadband light source (BBS), and the salinity exhibits a good linear relationship with the wavelength shift, with a coefficient of determination (R²) of 0.997. Meanwhile, the ring laser cavity improves the SNR of the sensing system from 22 dB to approximately 54 dB, and compresses the 3-dB bandwidth from 1.75 nm to 0.06 nm. Further adopting the support vector regression (SVR) algorithm for linear regression modeling of the spectral data, the results show that the mean absolute error (MAE) decreases from 0.50‰ to 0.04‰, the root mean square error (RMSE) decreases from 0.54‰ to 0.11‰, and R² reaches as high as 0.99988. To the best of our knowledge, this is the first work that combines salinity laser sensing with an artificial intelligence algorithm. The proposed sensor leverages the narrow linewidth and high SNR advantages of the FRL together with the high-precision linear fitting capability of the SVR algorithm, achieving significantly improved accuracy for salinity measurement compared to conventional spectral demodulation. Full article

(1) Background: Ultra-early optical coherence tomography (OCT) changes following intravitreal injection may reflect transient mechanical compression rather than pharmacologic effects; however, this temporal profile has not been rigorously characterised with appropriate statistical methodology. (2) Methods: In this prospective observational study, 40 eyes of 40 consecutive patients (one per patient) with macular edema secondary to neovascular age-related macular degeneration (nAMD), diabetic macular edema (DME), or chronic central serous retinopathy (CSR) underwent intravitreal bevacizumab (n = 35) or triamcinolone acetonide (n = 5). Goldmann applanation tonometry and spectral-domain OCT were performed at baseline, 2–5 min, 15 ± 5 min, 24 h, and 48 h post-injection. Repeated-measures ANOVA with Greenhouse–Geisser correction, linear regression, and Spearman rank correlation were applied. (3) Results: Central subfield thickness (CST) decreased markedly at 15 ± 5 min (mean −24.8 ± 11.5%; 95% CI: −28.5% to −21.1%; p < 0.001; partial η² = 0.70), with near-complete rebound by 48 h (−1.0%; p = 0.400). Peak intraocular pressure (IOP) elevation correlated with CST reduction (Spearman r_s = 0.61; 95% CI: 0.39–0.77; p < 0.001), and baseline CST predicted thinning magnitude (R² = 0.52; p < 0.001). (4) Conclusions: Ultra-early OCT thinning after intravitreal injection is consistent with transient mechanical compression. Retinal thickness measurements within 48 h post-injection should be interpreted with caution when assessing treatment response, as early anatomic reduction may not reflect pharmacologic efficacy. Full article

Machine learning approaches to wildfire spread prediction are constrained by the lack of standardized, multi-source, spatiotemporal datasets that fuse terrain, weather, and fire-state information into a single ML-ready format. We present WildfireCube, a reproducible event-centric pipeline and methodology for constructing dense fourth-order spatiotemporal tensors of shape (T, C, H, W) at 30 m spatial and 3 h temporal resolution. Following the analysis-ready data convention established in the Earth Observation community, the pipeline fuses four open data sources: the Copernicus GLO-30 Digital Elevation Model for static terrain derivatives, ERA5-Land reanalysis for hourly weather forcing, Sentinel-2 Level-2A imagery for spectral vegetation and burn-severity indices, and NASA FIRMS active-fire hotspot detections for fire-state reconstruction via ordinary kriging. The resulting 13-channel normalized tensor separates causal drivers into three physically motivated groups: static landscape controls (elevation, slope, aspect, fuel load), dynamic atmospheric forcings (wind components, temperature, precipitation), and evolving fire state (fire-front mask, burn severity, fractional burn, observation confidence). A physics-informed normalization framework maps all channels to bounded ranges using fixed physical constants rather than sample statistics, ensuring cross-event comparability and exact invertibility. We demonstrate the pipeline on 13 wildfire events across the United States, Canada, and Greece (2017–2023), producing a processed catalog exceeding 300 GB compressed and spanning a 14-fold range in burned area, a 27 °C range in mean temperature, and different fire regimes. Event tensors are stored in chunked Zarr archives with Zstandard compression, achieving a 2.58× compression ratio. As future work, the pipeline will be applied to a 40-event target catalog projected to exceed 2 TB of raw data, providing the multi-regime diversity and scale required for training robust deep learning models for spatiotemporal wildfire prediction. Full article

This study presents a comprehensive first-principles investigation of the structural, elastic, electronic, and optical behavior of cubic CsCaF₃ under hydrostatic pressure. The material is confirmed to be a stable Pm-3m fluoride perovskite, with a lattice constant of $4.496 \AA$ and a tolerance factor of $0.902$. At ambient conditions, CsCaF₃ exhibits high intrinsic stiffness ($C_{11}=107.88 \mathrm{G}\mathrm{P}\mathrm{a}$, $B=53.07 \mathrm{G}\mathrm{P}\mathrm{a}$, $G=29.16 \mathrm{G}\mathrm{P}\mathrm{a}$, $E=73.94 \mathrm{G}\mathrm{P}\mathrm{a}$) and maintains mechanical stability while becoming progressively stiffer under compression. The electronic structure reveals a wide indirect band gap of $7.1 \mathrm{e}\mathrm{V}$ that broadens to $8.43 \mathrm{e}\mathrm{V}$ and transforms into a direct gap at elevated pressures. Optical calculations show strong transparency in the visible range, with a low refractive index ($1.58$) and reflectivity ($~5\%$), and a deep-UV absorption edge near $6 \mathrm{e}\mathrm{V}$. Pressure enhances these features, increasing the refractive index to 1.66 and the maximum reflectivity to 45.87% at $24 \mathrm{G}\mathrm{P}\mathrm{a}$. The plasmon resonance also displays pronounced tunability, blue-shifting from $29.56$ to $30.79 \mathrm{e}\mathrm{V}$ with a fourfold rise in intensity. Analysis of the effective-electron number further indicates pressure-driven redistribution of spectral weight within the UV region. Together, these findings demonstrate that CsCaF₃ combines robust structural stability with highly pressure-tunable optical and plasmonic responses, positioning it as a promising candidate for deep-UV optoelectronics, photonic coatings, and pressure-responsive optical technologies. Full article

Accurate measurement and control of impurities are critical for ensuring the quality and therapeutic performance of solid-state pharmaceutical formulations. This study introduces a rapid, minimal sample preparation analytical approach for quantifying low-level dalmelitinib impurities in dalmelitinib mesylate, employing near-infrared (NIR) spectroscopy combined with partial least squares regression (PLSR). To mimic actual manufacturing conditions, a mixture system was designed comprising dalmelitinib mesylate, dalmelitinib impurity, and formulation excipients. Various spectral preprocessing strategies were systematically evaluated, including Savitzky–Golay first derivative (SG1st), Savitzky–Golay second derivative (SG2nd), multiplicative scatter correction (MSC), standard normal variate (SNV), wavelet denoising, wavelet compression, and their combinations. The optimal model was obtained using SG1st combined with wavelet denoising. The resulting PLSR model (7 latent variables) showed good predictive performance, with an R² of 0.99569 and an RMSECV of 0.00315. The limit of detection (LOD) and limit of quantification (LOQ) were 0.234% and 0.708%, respectively, demonstrating applicability for monitoring low-level impurities in pharmaceutical formulations. Method validation demonstrated satisfactory precision (RSD < 3%), accuracy (100.77–102.01%), and stability over 24 h (RSD ≤ 4.75%). Compared with conventional solid-state analytical techniques, the proposed NIR–PLSR framework enables rapid, non-destructive analysis with minimal sample preparation. The combination of derivative preprocessing and wavelet denoising improved extraction of impurity-related spectral information in complex pharmaceutical systems, highlighting the potential of this approach for process analytical technology (PAT) and pharmaceutical quality monitoring. Full article

Recently, artificial neural networks (ANNs) have shown impressive performance in the compressive hyperspectral imaging (CHI) reconstruction task, but the high energy consumption limits their deployment on energy-constrained devices. This paper develops a novel spiking neural network (SNN), termed spiking spectral-weighting reconstruction network (SSWR-Net), to significantly improve the energy–efficiency ratio in CHI reconstruction. Firstly, a spiking spectral-weighting convolution block is proposed to adaptively modulate the spiking signals, enabling the SNN to fit continuous spectral correlation curves. Secondly, a residual feature reuse module with more direct connections is designed to achieve efficient and lightweight spatial–spectral feature extraction. Thirdly, customized feature scaling architectures are introduced to resolve the dimensional mismatch issue and enhance information flow. Finally, we propose a novel temporal-wise progressive training method to optimize the multi-timestep SSWR-Net, which can significantly improve both training efficiency and reconstruction quality. Both simulation and real experiments demonstrate the superiority of the proposed method in both CHI reconstruction performance and energy efficiency. Specifically, SSWR-Net outperforms its ANN-based counterpart by 0.87 dB at a 19.74% energy cost. Full article

We develop a fast Chebyshev spectral collocation method for a coupled system of nonlinear Klein–Gordon equations augmented by Caputo-type fractional memory integrals. The governing equations retain the classical second-order time derivative as the leading operator and incorporate weakly singular convolution integrals modelling viscoelastic memory damping. The spatial discretisation employs Chebyshev–Gauss–Lobatto collocation, while the temporal integration uses a Newmark scheme (${\beta }_{\mathrm{NM}}=1/4$) combined with an implicit–explicit linearisation in which the linear spatial operator is treated implicitly and the nonlinear terms are treated explicitly through a second-order extrapolation. This linearisation eliminates the need for Newton–Raphson iterations at each time step. To overcome the dense memory bottleneck arising from two distinct fractional orders $\alpha \ne \beta$, the convolution memory kernels are compressed by independent sum-of-exponentials approximations obtained from a double-exponential quadrature of the kernel’s integral representation, which significantly reduces the computational complexity of the history term. A rigorous stability estimate and a global convergence bound are established using a discrete Grönwall inequality. Numerical experiments confirm the theoretical temporal and spatial convergence rates and demonstrate the practical speed-up afforded by the sum-of-exponentials acceleration. A solitary wave collision scenario illustrates the method’s capability to capture asymmetric dispersive wakes generated by the fractional memory. Full article

This work presents a Fourier-domain encryption scheme for multiplexed image databases that integrates virtual-optical multiplexing with chaotic diffusion. By combining chaotic encryption with spectral-domain symmetry reduction, the proposed approach secures large multiplexed image datasets while reducing memory requirements and preserving reconstruction fidelity. A dataset of 2025 grayscale images ($512\times 512$ pixels) is multiplexed and encrypted using linear chaotic transformations applied separately to the amplitude (A) and phase ($\varphi$) components. To improve storage efficiency, the symmetry conditions of both spectral components are exploited, allowing a reduced portion of the Fourier plane to be stored while preserving accurate reconstruction. A performance landscape relating the correlation coefficient (CC), memory consumption, and the retained Fourier-plane percentage (FPP) is constructed to identify stable operating regions that balance reconstruction fidelity and compression under increasing multiplexing load. The encryption key consists of a 22-symbol ASCII string from which 84 seed parameters for a deterministic pseudorandom chaotic map are derived. Security and sensitivity analyses demonstrate strong key dependence and resistance to statistical attacks, while maintaining high reconstruction fidelity. The proposed scheme provides an efficient and scalable solution for secure large-scale image repositories. Full article

As the role of remote sensing continues to grow, the fusion technology of low-spatial-resolution hyperspectral images and high-spatial-resolution multispectral images has become increasingly critical. Traditional methods rely on fixed rules and exhibit poor robustness, whereas deep learning methods struggle to establish efficient interactions between local and global information due to the complexity of their underlying networks. Therefore, we propose a deep learning fusion module that combines pixel-wise adaptive wavelet transform with a spectral–spatial dual-branch extraction. Firstly, by utilizing the unique properties of the wavelet transform, it is possible to effectively preserve spectral information and extract spatial edge features, thereby achieving preliminary fusion by leveraging both low-frequency and high-frequency components. To compensate for the lack of nonlinear expression capability in the wavelet transform, a dual-branch parallel extraction of spectral and spatial features is subsequently performed in the deep learning module. The Multi-Scale Group Convolution module (MSGC) is utilized to extract spectral information, while the Spectral Compression and Spatially Guided Gating Module (SCSGM) is employed to extract spatial information, thereby enhancing the data’s adaptive capability. A bidirectional attention mechanism is interspersed within the module to capture complementary information across different scales, ultimately reconstructing a high-resolution hyperspectral image. Finally, the proposed fusion strategy demonstrates superior performance in practical image reconstruction, outperforming more than ten state-of-the-art fusion methods. Full article

The training of physics-informed neural networks (PINNs) for nonlinear multiphase flow in porous media is hampered by gradient conflicts between the individual components of the composite loss function. To address this problem, we propose a weighted gradient consistency metric that jointly accounts for the magnitudes and directions of the gradients of each loss term. Theoretical estimates of the convergence rate are derived, relating the proposed metric to the spectral properties of the preconditioner. The method is evaluated through a comparative study of optimizers—Adam, L-BFGS, and self-scaled Broyden—applied to three formulations of increasing complexity: a linear Buckley–Leverett model, a compressible two-phase model, and a fully nonlinear model with non-Newtonian rheology. The experiments demonstrate that self-scaled methods consistently achieve higher gradient alignment, faster loss reduction, and improved approximation accuracy compared to standard quasi-Newton and first-order baselines. Full article