Search Results (481)

Transient-source detection without relying on difference images still faces challenges in achieving high accuracy, especially under practical space-based astronomical survey conditions where the data volume is enormous, on-orbit transmission bandwidth is limited, and real-time response is required for rapid follow-up observations. To address these issues, this paper proposes a lightweight detection network that integrates multi-scale feature fusion with contextual feature extraction, enabling efficient real-time processing on resource-constrained edge devices. The proposed model enhances robustness to point-spread-function variations across observation conditions and to complex background environments, while simultaneously improving detection accuracy. To evaluate performance comprehensively, lightweight VGG and lightweight ResNet architectures and other baseline models—commonly used as baselines for transient-source detection—are adopted for comparison. Experimental results show that under the condition that the models have approximately the same number of parameters, the proposed network achieves the best accuracy, obtaining nearly 1% improvement compared with the best-performing baseline model. Based on this design, an ultra-lightweight version with only 7k parameters is further developed by incorporating a compact multi-scale module, improving accuracy by 1% over the version without the multi-scale structure. Moreover, through heterogeneous knowledge distillation and adaptive iterative training, the accuracy of the ultra-lightweight model is further increased from 93.3% to 94.0%. Finally, the model is deployed and validated on an AI hardware acceleration platform. The results demonstrate that the proposed method substantially improves inference throughput while maintaining high accuracy, providing a practical solution for real-time, low-latency, on-device transient-source detection under large data volume and limited transmission conditions. Specifically, the proposed models are trained offline on a high-performance GPU and subsequently deployed on the Fudan Microelectronics 7100 AI board to evaluate their real-world inference efficiency on resource-constrained edge devices. Full article

In X-ray imaging, tissue scattering is an important factor that degrades image clarity, especially using a portable gridless X-ray imaging device. This study focuses on using Monte Carlo simulation to quantify the effect of scatter radiation on image resolution, by analyzing the point spread function (PSF) and the corresponding modulation transfer function (MTF). Lateral energy absorption profiles in tissue and a cesium iodide (CsI) scintillator were calculated at different X-ray tube voltages (70–90 kV) and filter configurations. Results showed that 85.7% of the total scattered radiation is concentrated at a distance of 4 cm from the central axis for the tissue and 67.37% for the CsI scintillator. The MTF remained high at low spatial frequencies (23% at 0.04 cycles/cm) but dropped at mid frequencies (0.015–0.025 at 0.3–0.6 cycles/cm) and was almost zero at high frequencies (0.004 at 0.8 cycles/cm), indicating loss of detail due to scattering. Increasing the thickness of the filter or adding a copper (Cu) filter reduced the contrast at low spatial frequencies (from 23% to 21%). The study quantitatively investigated the MTF degradation in portable X-ray imaging devices without grid, due to scatter. These results may aid in the development of scatter correction algorithms to improve image quality without the need for an anti-scatter grid. Full article

In ground-based short-wave infrared (SWIR) astronomical observations, temperature drift in the detector readout circuit often introduces nonlinear, spatially non-uniform stripe noise together with Gaussian noise, making weak stellar targets easily submerged and difficult to detect. To address this challenge, we propose a structurally guided weighted low-rank denoising method for infrared star images. Going beyond traditional spatial filtering and standard low-rank decomposition, the proposed method integrates physical priors with mathematical optimization into a unified framework. First, the point spread function (PSF) characteristics of stellar targets are used to construct a hierarchical structural filter, which is further transformed into adaptive prior weights. This design preserves weak-target energy while suppressing noise during iterative optimization. Second, by exploiting the global spatial correlation of the image, residual stripes and the background are jointly modeled as a low-rank component for effective separation. Finally, Bilateral Random Projection (BRP) is introduced to accelerate the weighted soft-thresholding iterations. Experiments on real ground-based observation data, together with ablation studies and sensitivity analyses, demonstrate that the proposed method effectively suppresses structured stripe interference while preserving weak stellar targets under low-SNR conditions. In addition, the acceleration module further improves computational efficiency, making the framework more suitable for practical real-time processing. Full article

Optical reflective markers are widely used in precision medicine, computer-assisted surgery, and robotic interventions. Nevertheless, intraoperative tracking still faces challenges such as sensor saturation, Point Spread Function (PSF) blooming, and flat-top artifacts, which affect localization precision and stability. Traditional deep learning detectors perform well in general object recognition but are limited in handling saturated infrared reflective markers due to their neglect of optical physics and inability to separate signal from blooming interference. This paper presents a physics-prior-guided network integrating a Brightness-Prior-Enhanced Spatial Attention (BPESA) mechanism for high-precision sub-pixel marker localization under saturation conditions. The method achieves a Root Mean Square (RMS) error of 0.52 pixels (approximately 0.11 mm) on a dataset of 8000 binocular images and reduces the localization error by approximately 54.4% compared with the baseline YOLOv8 model, while maintaining an inference speed of 134.6 FPS. The results demonstrate that optical blooming interference can be effectively mitigated by a learnable physics-prior branch, providing accurate marker coordinates that form a foundation for potential downstream tracking or navigation tasks. Full article

Haze removal methods based on the estimation of scene depth ratio in the Atmospheric Scattering Model (ASM) have achieved satisfactory results. However, the ASM ignores the blur equivalent to a point spread function caused by forward scattering. This paper proposes a simplified United Scattering Transmission Model (USTM), in which both forward scattering and back scattering are taken into consideration physically. It utilizes Taylor expansion to correlate the hazy image and its second-order operator with the dehazed image. Additionally, we establish a layered decomposition mechanism of the scattering medium; by fitting the limitation expression and the image signal at infinity, the parameters related to the inherent optical properties used in the model can be obtained. When the stable transmittance estimation approaches are applied into this USTM, the scene radiance can be restored effectively. We conducted evaluation experiments on datasets including RESIDE-RTTS (Real-world Task-Driven Testing Set), Haze4K, and DenseHaze, using metrics such as PSNR, SSIM, newly visible edges and the ratio of the gradients. The results demonstrate that USTM achieves satisfactory results across multiple evaluation dimensions. Regarding the core objective fidelity metric PSNR, it achieves an optimal score of 11.87 dB, representing an approximate 3.85% improvement over the second-best method. Compared to the traditional ASM, the USTM shows an average improvement of approximately 23.5% in edge restoration capability (newly visible edges) and an average improvement of approximately 18.1% in gradient fidelity (the mean ratio of the gradients). Furthermore, compared with advanced deep learning dehazing methods, our method remains highly competitive in edge and gradient restoration metrics, and its lightweight design provides excellent efficiency and compatibility with downstream tasks. The comprehensive results show that the USTM achieves effective improvements in both physical accuracy and detail restoration performance. Full article

Despite the growing demand for functional gluten-free (GF) foods, the application of Quercus pubescens acorn flour remains largely underexplored. This study addresses this gap by optimizing GF cookies using response surface methodology (RSM) and prepared with Q. pubescens acorn flour and xanthan gum to balance technological quality, sensory acceptability, and functional value. A three-level full factorial design (FFD) evaluated the effects of acorn flour proportion (0, 50 and 100%), and xanthan gum level (1, 2 and 3%) on physicochemical properties (moisture, water activity, color, texture, and dimensions), sensory attributes using a 9-point hedonic scale, proximate composition, and bioactive and antioxidant properties (total polyphenols, tannins, DPPH, ABTS, FRAP). Linear and quadratic polynomial models adequately described the experimental data (R² = 0.86–0.99; non-significant lack of fit). Increasing acorn flour content significantly intensified cookie darkening, reduced snapping force and bending stiffness, reduced spread factor, and affected sensory perception, while xanthan gum improved structural integrity and dimensional stability. Multi-response optimization identified an optimal formulation containing 41.05% acorn flour and 1.46% xanthan gum, achieving balanced color development (darkness index ≈ 62), bending stiffness (~38 N/mm), and high overall sensory acceptability (~7.8). The optimized GF cookies exhibited a favorable nutritional profile and antioxidant properties, characterized by elevated total polyphenol content and antioxidant capacity, confirming the functional potential of acorn flour. The optimized cookies (containing 41.05% acorn flour) exhibited a six-fold increase in total phenolic content (from 1.63 to 10.08 mg GAE/g) and 8–10 times higher antioxidant capacity (DPPH, ABTS, and FRAP assays) compared to the control, confirming the substantial functional potential of Q. pubescens in gluten-free systems. Full article

The spatial response of rectangular pulse heterodyne phase-sensitive optical time-domain reflectometry ($\varphi$-OTDR) to an acoustic event is characterized by a windowing function rather than a point-like sensitivity. This effect degrades the system’s spatial resolution and introduces systematic errors in array signal processing. This work presents modeling analysis and a mitigation strategy for this fundamental limitation. The spatial windowing effect is modeled as a point spread function (PSF) derived from physical mechanisms and system parameters, including the pulse width, gauge length, and intra-pulse intensity dynamics. The PSF model is validated against measurements under near-ideal conditions using a fiber-coupled tuning fork. A Wiener filter-based deconvolution method is utilized to invert the windowed spatial response towards a point-like response. The effectiveness of this inversion is demonstrated through enhanced spatial resolution and accurate reconstruction of two-dimensional wavefront geometry. Furthermore, the impact of this effect on array signal processing is quantitatively evaluated. The results demonstrate that the proposed method effectively suppresses systematic errors in wavefield analysis, and specifically enhances the accuracy and confidence of steered response power—phase transform (SRP-PHAT) spatial spectrum estimation. This study provides a systematic framework for understanding, quantifying, and inverting the spatial response in $\varphi$-OTDR, enabling accurate and interpretable acoustic field sensing. Full article

In industrial acoustic imaging, microphone array design is often limited by the strong frequency dependence of array performance, the high computational cost of optimization, and the expense of deploying large numbers of microphones. Most existing optimization-based methods require simultaneous optimization of all array elements, resulting in long design times and limited flexibility in controlling element count. To overcome these limitations, this paper proposes a fast and iterative microphone array design method using equilateral triangular subarrays as basic units. Instead of optimizing the entire array at once, the proposed method incrementally adds subarrays, and in each iteration, a genetic algorithm optimizes only the placement of the newly added subarray for a specified target frequency. By exploiting the rotational symmetry of the equilateral triangular subarrays and the geometric characteristics of the array point spread function, the number of optimization variables and the computational domain are significantly reduced, enabling efficient array design. The proposed method allows frequency-specific performance optimization while providing direct control over the number of array elements, achieving a practical balance between imaging performance and hardware cost. Comparative results show that arrays designed using this method generally exhibit improved main lobe width and sidelobe level performance near the target frequencies compared with several classical array configurations. Full article

This paper addresses the critical challenge of detecting weak, small targets in sonar intensity images for linear-array active sonar, where target signatures are not only obscured by low signal-to-interference ratio (SIR) but also strongly resemble structural interference arising from beamforming processing. We propose an end-to-end detection method that integrates controllable simulation with spatiotemporal structure-aware modeling. First, a physics-informed simulation system is constructed, centered on the Bellhop ray-tracing model. It incorporates multiple environmental effects, including multi-highlight targets, spectrally shaped noise, range-dependent reverberation, discrete scatterers, multipath propagation, and platform perturbations. Through closed-loop SIR calibration and point spread function (PSF)-constrained automatic annotation, a high-fidelity dataset with traceable parameters is generated. Second, the YOLOv8-Mamba-P2 detection network is designed. It introduces gated long-range spatial mixing modules (inspired by Mamba) to model global context and enhance the ability to discriminate interference structures, and extends a P2 small-scale detection branch to improve the perception and localization capabilities for weak targets. This enables precise target detection within complex backgrounds. Experimental results demonstrate the algorithm’s superior performance in low-SIR and strong reverberation conditions, achieving significant improvements in recall and localization accuracy while maintaining real-time inference efficiency, offering a promising framework for sonar target detection under the simulated conditions considered, with potential applicability to complex marine environments pending further real-world validation. Full article

DUR (Deep Underground Railways) stations, such as Suseo Station in Korea, present unique evacuation challenges stemming from multi-level spatial depth, long vertical circulation paths, and rapid smoke spread dynamics. Conventional design guidelines often fail to capture these complexities, underscoring the need for advanced, simulation-driven safety evaluation frameworks. This study proposes a comprehensive Digital Twin-based methodology that integrates spatial topology modeling, agent-based evacuation simulation, and dynamic hazard-aware routing. A multi-layer map topology was constructed from high-fidelity architectural geometry, decomposing the station into functional regions and encoding connectivity across platforms, concourses, corridors, and vertical circulation elements. Real-time hazard conditions were reflected through dynamic adjustments to edge weights, allowing evacuation paths to adapt to blocked exits, fire shutter operations, and smoke-infiltrated domains. Ten evacuation scenarios were developed to assess sensitivity to fire origin, exit availability, vertical circulation failures, and onboard passenger loads. Simulation results reveal that evacuation performance is primarily constrained by vertical circulation bottlenecks, with emergency stairways (E1 and E2) serving as critical choke points under high-density conditions. Cases involving exit closures or fire-compartment failures produced significant delays, frequently exceeding NFPA 130 and KRCODE performance criteria. Conversely, guided evacuation strategies demonstrated marked improvements, reducing congestion and enabling compliance with platform evacuation thresholds even in full-load scenarios. These findings highlight the necessity of transitioning from static design evaluations toward Digital Twin-enabled, predictive safety management. The proposed framework enables real-time visualization, intervention testing, and operator decision support, offering a scalable foundation for next-generation evacuation planning in extreme-depth railway infrastructures. Full article

To evaluate whether Bangerter foils (BFs) could serve as a low-cost myopia control intervention, we measured and compared the imaging properties of fifteen BFs from two manufacturers, four myopia control spectacles, and a single-vision lens. Image quality metrics related to light-signaling theories of myopia onset and progression were evaluated in three tests: i. the assessment of the focusing properties through the maximum of the point spread function (MaxPSF), modulation transfer function (MTF), and area under the MTF (AUMTF); ii. the quantification of the scattered light (s) using the optical integration method; and iii. the calculation of the Michelson contrast (MiC) on a binary grating imaged under dark and bright illumination. BFs exhibited lower MaxPSF, AUMTF, and MTF values than the myopia control lenses. Except for one 0.6-graded BF, none of the other BFs mimicked the scattering behavior of the diffusion optics technology lenses. Moreover, BFs showed lower MiC values than with myopia control lenses under both lighting conditions. Although the BFs did not replicate the imaging properties of myopia control lenses, they still demonstrated effective contrast reduction across the lighting conditions. Whether they may help to slow myopia progression remains uncertain, perhaps even unlikely, given the fundamental imaging differences. Full article

The aim of this research is to formulate and analyze a modified $S{I}_{p}IVR$ mathematical model to study the transmission dynamics of poliovirus and assess the impact of vaccination on disease control. The proposed model extends classical SEIV-type frameworks by incorporating a recovered compartment with long-term immunity and by replacing the traditional exposed class with a pre-infectious compartment ($I_p$) that captures silent viral shedding during the incubation phase of poliovirus. This modification addresses the critical epidemiological feature that individuals can transmit the virus before showing symptoms while maintaining biological accuracy in compartment definition. Several fundamental analytical properties are rigorously established, including positivity, boundedness, and the existence of a biologically meaningful invariant region. The basic reproduction number $R_0$ is derived using the next-generation matrix approach, and comprehensive stability analysis is carried out. The analysis shows that the DFE is locally and globally asymptotically stable whenever $R_0<1$. Using center manifold theory, a forward bifurcation is rigorously demonstrated, indicating that disease persistence emerges smoothly as $R_0$ crosses unity. Local and global sensitivity analyses of the basic reproduction number $R_0$ identify critical epidemiological parameters, and points to vaccination coverage and transmission rates as key drivers of outbreak dynamics. Numerical simulations confirm the analytical results and illustrates two different epidemiological scenarios, one with $R_0<1$, and another with $R_0>1$ along with neural network analysis by using the same data from both cases in a built-in function package in MATLAB-2020 software. It utilizes all of its hidden layers to check the data used by the model for validation performance and training and to find the absolute and mean squared errors. It also shows how vaccination suppresses the spread of infection. These findings provide a strong mathematical basis for public health policy, offering strategic insight into how vaccination campaigns might be optimized to accelerate progress toward global polio eradication. Full article

Volcanic magma plumbing systems is essential for understanding crustal–mantle material exchange and the dynamics of volcanic activity. The magnetotelluric method (MT) offers an effective tool for imaging conductive features from the crust to the lithospheric mantle. However, current survey strategies face a tradeoff between imaging resolution and acquisition cost. Here, we construct a lithosphere-scale synthetic model of a magma plumbing system and use 3D MT inversion, sensitivity analysis, and point spread function evaluation to assess the resolving capability of sparse versus dense arrays. Our results show that large-scale conductive anomalies in the mid–lower crust and lithospheric mantle can be reliably imaged using a sparse regional array with targeted densification in the crustal anomaly zone. This approach reduces field costs and computational demand. Guided by these findings, we conducted MT observations across the Longgang volcanic field and identified low-resistivity anomalies extending from the lithospheric mantle into the mid–lower crust. These features are consistent with the dense array MT inversion results. Our study demonstrates that an array strategy combining wide-area sparse coverage with targeted densification offers a cost-effective approach to image deep conductive structures, which may provide practical guidance for optimizing MT survey design in volcanic regions. Full article

Zernike polynomials constitute an essential mathematical basis for representing functions defined over the unit disk. They are widely used in a diverse range of scientific and engineering disciplines, including adaptive optics for characterizing atmospheric distortions, ophthalmology for quantifying ocular aberrations, microscopy for instrument characterization and aberration correction, and optical metrology for surface profiling. This paper introduces ZernikeViewer, a software framework developed for the rapid calculation and visualization of fringe, phase, and point spread function (PSF) maps from Zernike coefficients. The framework leverages CPU multicore and multithreading capabilities through the .NET Task Parallel Library (TPL), augmented by codebase optimizations and the preloading of precomputed Zernike polynomial matrices. These optimizations reduce computation time by a factor of 7 to 10 compared to a conventional approach; for instance, from 1 ms to 0.1 ms for a radial order of n = 10 and from 700 ms to 80 ms for n = 100. Numerical error analysis confirms the accuracy of the computation, with an average root-mean-square (RMS) error of 0.11 ms observed in the timing measurements. Furthermore, it is demonstrated that implementing Jacobi recursion relations could potentially reduce the numerical calculation error by up to 5 orders of magnitude. Full article

Off-axis reflective telescopes are prone to component misalignment due to external environmental factors and mechanical vibrations. This misalignment introduces low-order aberrations, which severely degrade imaging quality. Thus, active misalignment correction is crucial for maintaining the imaging performance of off-axis reflective telescopes. Current computer-aided alignment technologies for optical systems mostly rely on wavefront sensors to acquire aberrations at multiple fixed fields of view (FOVs) or even the full FOV. This significantly increases system complexity and hinders practical engineering applications. To address this issue, this study first conducts sensitivity analysis of misaligned degrees of freedom (DOFs) using a mode truncation algorithm based on singular value decomposition (SVD). A compensation strategy is proposed to avoid the aberration coupling effect. Furthermore, two novel misalignment aberration compensation methods for off-axis reflective telescopes are presented. These methods require only a single focal spot image and eliminate the need for aberration detection and iterative calculations. One method directly solves component misalignment errors using a convolutional neural network (CNN) based on the system’s point spread function (PSF). To further improve compensation performance, an improved method fusing spot images and Zernike coefficients is proposed. In practical misalignment correction, both methods input a single acquired focal spot image into a well-trained model to obtain the misalignment compensation amount. Simulation experiments demonstrate that the improved method, which uses Zernike polynomial coefficients as an intermediate feature bridge, effectively establishes the mapping relationship between spot images and misalignment amounts. It achieves higher solution accuracy and better aberration compensation effect compared to the direct CNN method. This verifies the necessity of extracting Zernike polynomial coefficient features from spot images. Comparative experiments with the traditional sensitivity matrix method show that the two proposed methods outperform the sensitivity matrix method in aberration compensation accuracy over a large misalignment range. Comprehensive simulation results confirm the feasibility and effectiveness of the proposed methods. They overcome the limitations of existing methods, such as complex structure, high cost, and low efficiency, to a certain extent. Full article