Search Results (288)

The goal of agrivoltaic engineers is to use growing space for the synergistic production of both food and energy, typically via photovoltaic (PV) capture. Most research in this area has been carried out in arid, high-light environments, but subtropical and temperate regions are also critical production zones, and installation designs vary considerably. In this study, tomato and lettuce production using an agrivoltaic high tunnel (HT) design specific for a subtropical environment (NE Georgia, USA, USDA Zone 8A) was tested using organic production standards. The design utilized typical HTs (approx. 11 m × 5 m) with solar panel arrays hung internally. The design aimed to (1) meet off-grid power needs, (2) mitigate excessive temperature and humidity, (3) balance shade and plant productivity, and (4) simplify installation and maintenance. Treatments were replicated at the HT level, and cultivar differences were assessed to identify genotypes that might serve in future work to optimize yield under partial shade. In 2023 and 2024, we employed novel organic photovoltaic (OPV) panels, which are partially opaque. The OPV panels provided sufficient energy needs to maintain beneficial conditions without external power sources. In 2024, tomato plants in the OPV HTs experienced an area-weighted daily light integral (DLI, mol photons m⁻² d⁻¹) of approximately 31.8 (95% CI [28.9, 34.7]), compared to 34.7 (95% CI [31.8, 37.6]) in non-OPV HTs, an approximate reduction of 8%. Average maximum temperatures in the OPV HTs were 33.5 °C (95% CI [30.6, 36.4], compared to 35.1 °C (95% CI [30.9, 39.2]) in the non-OPV HTs, an approximate reduction of 1.6 °C. In 2023, tomato marketable yield was reduced by approximately 0.9 kg per plant in OPV HTs compared to non-OPV HTs (p = 0.023). In 2024, yields were statistically equivalent across all treatments (p > 0.1), while marketable fraction was improved relative to 2023 and was greatest in the HTs. Lettuce yield for both years was unaffected by the presence of HTs or OPV panels (p > 0.1). In 2025, we conducted an additional experiment using a shade-equivalent array of conventional 100% opaque photovoltaic (PV) panels and observed a similar reduction in DLI and no significant impact on tomato yield parameters (p > 0.1 Both designs were effective at equilibrating conditions inside the HTs to ambient temperature levels outside the tunnels. Using results from the study, an app for agrivoltaic value estimation was developed. Based on that software, the presented agrivoltaic design under currently available silicon–PV technology achieves an 18% annual return, assuming system depreciation is minimal and surplus energy could be applied to other on-farm needs. Full article

High-quality X-ray pulsar observation profiles are vital for investigating both their physical properties and navigation applications. Conventional profile extraction relies on epoch folding, whose performance is constrained by observation duration and bin size, often leading to poor-quality profiles or even failure under extremely low-photon conditions. This paper proposes a novel method that directly extracts high-quality profile frequency spectra merely by statistical analysis of photon sequences followed by the reconstruction of time domain waveforms. Monte Carlo simulations and real observational data demonstrate that the proposed method exhibits higher correlation coefficients and signal-to-noise ratios than those obtained using traditional epoch folding, and also outperforms the Fourier-series-based frequency cutoff method. Moreover, comparable profile quality can be achieved using an order of magnitude fewer photons than required by epoch folding. The lower the photon count, the more significant the improvement, making the method especially suitable for small-area detectors and resource-constrained observation scenarios. Full article

To address the issue of the Geiger-mode Avalanche Photodiode (GM-APD) LiDAR’s echo being easily overwhelmed by strong noise under low signal-to-background ratio conditions, leading to degraded performance in range parameter estimation and low target restoration accuracy, this paper proposes a range parameter estimation method based on multi-scale tracking differentiator. This method eliminates the reliance on complex statistical models and spatial prior information and uses a nonlinear dynamic tracking mechanism to extract target information. Firstly, a dual-scale tracking differentiator system is constructed, where the large-scale factor captures the transient mutation characteristics of the echo signal, and the small-scale factor estimates the overall evolution trend of the signal. Secondly, the difference between the dual-scale outputs is obtained to acquire the residual signal, and nonlinear mapping enhancement is performed in combination with the photon trigger probability characteristics to deeply suppress noise and highlight the target peak. Finally, the peak threshold method is used to complete the range calculation. Simulation results show that when the SBR = 0.06, compared with typical methods such as the neighborhood kernel density method, the method in this paper is more robust, the root mean square error of the range estimation is reduced by at least 38.35%, and the target restoration degree is improved by at least 19.99%, which provides a highly efficient way for high-fidelity single-photon three-dimensional imaging and target detection under strong noise. Full article

Recent studies have proposed improving Laser Speckle Contrast Imaging (LSCI) by using polarimetric filtering to isolate multiply scattered photons from moving red blood cells (RBCs), an approach referred to as Laser Speckle Orthogonal Contrast Imaging (LSOCI). This reliance on multiple scattering enables the development of a calibration method based on a moving reference sample, chosen to generate dynamic circular Gaussian speckle fields that replicate the statistical properties of RBC scattering in both intensity and the distribution of polarization states. Assuming that multiply scattered photons from both RBCs and the reference sample exhibit a homogeneous distribution of polarization states over the Poincaré sphere, the proposed calibration links in vivo speckle contrast reduction bijectively to an equivalent speed of the reference sample. We demonstrate that this equivalent-velocity metric yields consistent in vivo measurements across distinct instruments despite the use of different laser spectral widths, thereby providing a standardized and transferable means to quantify microcirculatory activity. Full article

Accurate nanoscale displacement readout is essential for optical inertial sensors, precision positioning, and micro-vibration characterization. In this work, we develop a free-space optical heterodyne interferometric readout system for low-frequency nanoscale displacement sensing and establish an SNR-guided adaptive demodulation framework. Two complementary demodulation strategies are integrated: Bessel-function-based frequency-domain sideband extraction for small-amplitude low-SNR motion and IQ quadrature phase tracking for larger-amplitude displacement. The experimentally demonstrated framework maps the applicability regimes of the two methods and enables wavelength-referenced displacement readout over a range from sub-nanometer narrowband detection to 250 nm under the present experimental conditions. The implemented system achieves a repeated-measurement repeatability of 0.40 nm under a 10 Hz excitation condition, and spectral SNR analysis is consistent with time-domain statistical evaluation. Finally, the readout system is applied to a quartz pendulum inertial structure, demonstrating its potential for photonic displacement sensing and optical inertial sensor characterization. Full article

To address the challenge of accurately reconstructing multi-depth targets using single-photon Light Detection and Ranging (LiDAR) under few-frame conditions in high-precision applications such as autonomous driving perception, remote sensing, and military reconnaissance, this paper proposes a computational imaging method named the Jensen–Shannon Divergence Weighted Pixel Fusion Constant False Alarm Rate (JSWPF-CFAR) approach. First, the proposed method utilizes the Jensen–Shannon (JS) divergence to characterize the statistical similarity between adjacent pixels, thereby constructing adaptive weights to achieve the effective fusion of echo signals. The key innovation lies in the formulation of a JS divergence-based weighting factor, which fully exploits the inherent spatial correlation within 3D target structures to optimize the pixel fusion process and enhance the signal statistics of target echoes. Subsequently, a CFAR detection model tailored for Geiger-mode Avalanche Photodiode (GM-APD) multi-depth echo signals is constructed to estimate the noise photon count within a local sliding window; this estimate is then used to calculate a photon counting threshold for identifying and extracting high-confidence target intervals. Finally, a peak-picking method is employed to perform the 3D reconstruction of multi-depth targets. Compared with existing techniques such as matched filtering and Reversible Jump Markov Chain Monte Carlo (RJMCMC), the proposed method exhibits superior reconstruction quality under few-frame and low Signal-to-Background Ratio (SBR) conditions. The experimental results demonstrate that the proposed method achieves an improvement in target restoration degree (RD) of at least 21.16% and a relative variance (Var) optimization of at least 62.90% over the matched filtering and RJMCMC baselines. These results indicate that the proposed approach effectively enhances the multi-depth estimation performance of single-photon LiDAR in complex scenes. Full article

Spectral chest radiography with photon-counting detectors (PCDs) enables energy-resolved acquisition for bone/soft-tissue separation, but quantitative performance depends on detector cross-talk and the selected material decomposition algorithm. We performed a controlled simulation study comparing a conventional low-order polynomial decomposition model with two machine learning regressors (multilayer perceptron (MLP) and support vector regression (SVR)) for a cadmium telluride (CdTe) PCD. A Geant4-derived detector response model, coupled with a charge-transport model, was integrated into a physics-forward model including charge sharing and Poisson quantum noise. Digital LucAl/IEC 62220-2-1 phantoms with aluminum and polymethyl methacrylate inserts were used for quantitative bias/root mean square error (RMSE) evaluation, and task-based low-contrast detectability that was evaluated using an exponential transformation of the free-response operating characteristic (EFROC) method using a matched-filter template. Performance was evaluated over clinically relevant dose levels (0.07–7.5 mAs), calibration grid densities ($3\times 3$ to $8\times 8$), and numbers of energy thresholds ($M=2$–6). Polynomial decomposition was stable under sparse calibration, whereas ML methods benefited strongly from denser calibration and additional thresholds; SVR achieved the lowest RMSE under dense calibration, while MLP produced smoother maps and improved soft-tissue detectability at low-to-intermediate dose. At high dose, all methods approached near-ideal detection performance. These results quantify practical trade-offs between calibration requirements, quantitative accuracy, and low-contrast detectability for PCD-based spectral chest radiography. Full article

We study underwater optical communication under photon-counting (Poisson) detection with realistic attenuation, background radiance, directionality, and pointing uncertainty. Information is embedded in (i) a temporal dictionary of pseudo-random noise (PRN) intensity sequences and (ii) an optional spatio-temporal extension, denoted SIM–TS (spatial-index modulation with temporal signaling), that combines temporal coding with spatial indexing across multiple transmit/receive apertures. For a fixed optical energy-per-symbol (photon budget), these structured waveforms increase observation dimensionality and improve maximum-likelihood separability under Poisson statistics. We present a layered modeling framework, derive the corresponding Poisson detection metrics, and use Monte Carlo evaluation to extract maximum range at a target symbol error rate. The results show that dimensional signaling provides a modest but repeatable gain in clear-water photon-limited regimes: at 100 kbps, SIM–TS increases the clear-water range from 593.8 m to 617.2 m at 450 nm (3.95%) and from 457.8 m to 473.4 m at 420 nm (3.41%) under fixed total power. In coastal water the gain falls below 1%, while in the 1 Gbps benchmark SIM–TS under fixed total power remains within about 2% of on–off keying (OOK) and the larger improvement under power combining is attributable primarily to increased photon budget. These rate–range trade-offs clarify when dimensional signaling yields practical gains and when attenuation, background, and misalignment dominate the link budget. Full article

Wafer-level testing of Photonic Integrated Circuits (PICs) represents a critical throughput bottleneck in silicon photonics manufacturing, particularly as co-packaged optics demand testing of thousands of optical I/O per wafer. This work introduces optimized alignment algorithms for the Technoprobe Eclipse Dynamic probe card system, which integrates electrical probes and a piezoelectrically actuated fiber array unit within a single probe head, eliminating external positioning equipment. We systematically evaluate seven alignment algorithms: Reference Coarse Scan, Reference Coarse+Fine Scan, Cross Scan, Local and Global Bayesian Optimization, Variable and Fixed Gradient Ascent. The evaluation is made across 72 simulated test cases derived from eight experimental datasets through systematic spatial windowing, combined with experimental validation. Performance is assessed under four operating regimes—high-speed (HS) and low-speed (LS) operation, each with or without hysteresis compensation (H/NH). Experimental validation across eight die positions confirms 100% success rate for both Local Bayesian (98.24% accuracy in 99.87 arbitrary units (a.u.)) and Fixed Gradient (99.18% accuracy in 154.01 a.u.) baseline algorithms. Comprehensive simulation results with improved algorithms across all four scenarios reveal distinct performance characteristics. Fixed Gradient achieves the highest reliability (95.8%) with 99.4% average accuracy across all operating conditions. Variable Gradient provides the fastest alignment (1.18 a.u. in HS-NH) with 90.3% reliability. Local Bayesian demonstrates 94.4% reliability with intermediate performance. Global Bayesian Optimization achieves the best sample efficiency (average 24 steps) but exhibits scenario-dependent reliability ranging from 88.9% (HS-H, LS-H) to 93.1% (LS-NH). For the ideal production scenario, high speed with effective hysteresis compensation (HS-NH), Fixed Gradient emerges as the optimal choice, delivering 95.8% reliability with 1.44 a.u. alignment time, resulting in the best success rate while being nearly as fast as the fastest method. Variable Gradient achieves the absolute fastest alignment (1.18 a.u.) but with 5.5% lower reliability (90.3%), making it suitable only for applications tolerating higher failure rates. Under realistic production conditions with uncompensated hysteresis (HS-H), Fixed Gradient maintains its advantage (95.8% reliability, 3.32 a.u.), while Global Bayesian degrades significantly (88.9% reliability, 4.29 a.u.). Statistical analysis using data profiles validates these methods for high-volume PIC manufacturing, with the Eclipse Dynamic system demonstrating per-die optical alignments in sub-second timescales using open-loop control hardware. Full article

In single-photon detection, dark count represents a critical limitation, particularly for high-sensitivity applications. Conventional estimators based on the binary per-gate observable become ill-conditioned when the dark count per-gate probability approaches unity, a situation common in first-photon-gated detectors with extended gate width. This work proposes a complementary characterization method based on the statistical expectation of dark count arrival time. This approach captures the cumulative temporal behavior of dark count across multiple gating cycles, providing a more accurate estimation of the dark count rate. Both numerical simulations and experimental results demonstrate that our method yields significantly more stable and precise measurements compared to the conventional approach. Specifically, while the conventional method introduces errors up to ±4% at larger gate widths, the proposed timing-based method converges to a significantly lower residual error of approximately −0.17%. These findings offer a promising route to enhance the characterization and performance of first-photon-gated single-photon detectors in practical applications. Full article

Two-photon microscopy enables in vivo imaging of astrocytic Ca²⁺ activity, yet detecting weak, transient, and background-coupled signals remains challenging due to low signal-to-noise ratios and heterogeneous noise. Here, we propose a low-parameter, adaptive framework for detecting weak astrocytic Ca²⁺ signals in two-photon imaging. After short-window frame accumulation, static background suppression, and Gaussian smoothing to stabilize intensity statistics, signal candidates are identified via segment-wise Gaussian mixture modeling, temporal persistence masking, and adaptive threshold updates. In simulated videos, the proposed method improved the Dice coefficient from 0.06 to 0.77 and increased the reference SNR from −9.82 to 3.40 dB. In in vivo recordings, the local SNR increased from 5.58 to 7.28 dB. Compared with fixed thresholding, AQuA, and AQuA2, our method was more robust under high-noise conditions while requiring only three user-defined parameters (minimum area, minimum duration, and an initialization coefficient). This framework provides an interpretable and computationally practical front-end module for the robust extraction of astrocytic Ca²⁺ signal in low-SNR two-photon imaging. Full article

We present the performance of a middle atmosphere Rayleigh lidar system that employs three receiver channels. We characterize the biases in the density and temperature profiles retrieved from each of the receiver channels as well as the combined receiver signal. We associate these biases with pulse pile-up, gain switching, and variations in the detector gain due to signal amplitude. We use a top-down temperature convergence methodology to determine the upper altitude up to which the signals should be compensated for the variations in detector gain. We find that the channels have warm biases in their temperatures of 2–8 K at 40 km. These biases decrease to between 1 K and 3 K at 60 km. Uncertainty estimates derived from the photon-counting statistics indicate temperature uncertainties on the order of 2–5 K in the 40–70 km region, which are consistent with the observed level of inter-channel variability after correction. A comparison with MERRA-2 reanalysis indicates an overall agreement in temperatures and differences that are consistent with the comparisons between the Rayleigh lidars and MERRA-02 at other sites. These results demonstrate that the proposed approach proves reliable for processing the multi-channel Rayleigh lidar data, particularly for systems employing more than two detection channels, and improves the fidelity and accuracy of the temperature retrievals. Full article

Machine learning (ML) is transforming the analysis of biomolecular data, holding significant promise for improving the efficiency and accuracy of microscopy image analysis and for studying the dynamics of molecules in live cells. As data-driven approaches continue to evolve, they may eventually replace traditional statistical methods that rely on conventional analytical methods. This review examines and critically analyses the state of the art of ML techniques as applied to various levels of data supervision in the analysis of dynamic single-molecule datasets obtained using superresolution optical microscopy. Collectively encompassed under the umbrella of “nanoscopy”, these methods currently comprise targeted techniques such as stimulated emission depletion (STED) microscopy and stochastic techniques like single-molecule localization microscopies (SMLMs), comprising photoactivated localization microscopy (PALM), DNA points accumulation for imaging in nanoscale topography (DNA-PAINT) microscopy, and minimal fluorescence photon flux (MINFLUX) microscopy. These techniques all enable the imaging of subcellular components and molecules beyond the diffraction limit, and some are additionally capable of studying their dynamics in real time, as reviewed here, using several ML techniques that facilitate motion analysis in two or three dimensions with qualitative and quantitative characterisation in the live cell. It is expected that the growing use of learning-based approaches in biological microscopy data processing will dramatically increase throughput and accelerate progress in this rapidly developing field. Full article

Bohr’s correspondence principle acts as a link between quantum physics and classical physics theory, while squeezed light, as a special nonclassical quantum state in quantum physics, achieves precision measurements and gravitational wave detection by minimizing quantum noise in one quadrature component of the optical field. Consequently, determining whether the classical counterpart of the squeezing operator reflects classical spatial scaling transformations is of significant theoretical importance. This paper establishes a universal integral formula that transforms any operator into its Weyl ordering form using the method of integration within the ordered product of operators, combined with the coherent state representation and integration theory within Weyl ordering. By transforming both single-mode and two-mode squeezing operators into their corresponding Weyl ordering forms, their classical counterpart functions are derived. This elucidates the classical correspondence of the squeezed light field density operator and demonstrates that this correspondence fundamentally represents a classical scaling transformation. As a practical application of the classical counterpart of the single-mode squeezing operator, the photon number distribution characteristics in a single-mode squeezed light field are obtained, confirming its noise-squeezing effect. This study not only deepens the theoretical implications of Bohr’s correspondence principle from the perspective of “transformation correspondence” but also introduces novel insights into the establishment of the mathematical foundations of quantum optics and quantum statistical theory. Full article

γ-photon tomography, which leverages the high penetration and electrical neutrality of high-energy γ-photons, offers a promising non-contact approach for industrial flow field monitoring. However, γ-photon flow-field images are inherently grayscale and exhibit probabilistic statistical imaging characteristics, leading to color banding artifacts when processed by mainstream colorization algorithms like DeOldify, which compromise structural continuity and visual consistency. To address this issue, this paper proposes a Structure Enhancement Colorization Network (SECN) model for γ-photon flow-field image colorization. A U-Net + GAN framework is employed, with ResNet101 as the generator backbone. It integrates structure-aware enhancement and multi-scale attention modules, while the discriminator incorporates enhanced blocks for improved boundary and texture discrimination. By adaptively fusing global–local features across channel and spatial dimensions, the SECN model effectively suppresses color banding artifacts and enhances structural consistency. To validate the effectiveness of the proposed algorithm, two CFD-simulated γ-photon flow-field image colorization scenarios—namely a large-scale vortex wake and a horizontal wake—are used as evaluation targets. In terms of image quality metrics, the proposed colorization algorithm achieves PSNR, SSIM, FID, and MAE values of 32.5831, 0.8612, 17.8514, and 0.0191, respectively, corresponding to improvements over DeOldify of 4.54%, 2.82%, 5.18%, and 11.16%. When considering information entropy, the proposed colorization algorithm achieves an average entropy value of 4.0257, marking a 4.44% increase compared to DeOldify’s 3.8543, demonstrating superior information preservation and reduced uncertainty in reconstructing complex probabilistic structures. Furthermore, from the perspective of parameter inversion, the temperature inversion MAPE is 7.60%, which is a significant reduction of 18.42% compared to that of DeOldify. Full article